# Import the necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import os

# Load the dataset from Colab
from google.colab import files
uploaded = files.upload()

Saving multi_method_importance.csv to multi_method_importance.csv

# Read the dataset
df = pd.read_csv('Health Dataset5.csv')
train_all = pd.read_csv('train_all.csv')
test_all = pd.read_csv('test_all.csv')
train_df_transformed = pd.read_csv('train_df_transformed.csv')
test_df_transformed = pd.read_csv('test_df_transformed.csv')
df_transformed = pd.read_csv('df_transformed.csv')
df_combined_with_country = pd.read_csv('df_combined_with_country (1).csv')
df_lagged = pd.read_csv('df_lagged.csv')
df_combined = pd.read_csv('df_combined (1).csv')
multi_method_importance = pd.read_csv('multi_method_importance.csv')
df_model_comparison = pd.read_csv('df_model_comparison.csv')

import statsmodels.api as sm
import matplotlib.pyplot as plt
from scipy import stats

# List of predictors (make sure column names match exactly in your dataframe)
features = [
    'Income', 'GDP', 'CPI', 'Sex ratio',
    'BMI (female)', 'Cost of a healthy diet', 'Inflation',
    'Incomplete tertiary education', 'Gini coefficient', 'Median age'
]

# Loop through each target variable
for target in ['Life expectancy', 'Cardiovascular diseases', 'Diabetes']:
    print(f"\nModeling for: {target}")

    # Subset and drop rows with missing values
    model_data = df[[target] + features].dropna()
    X = model_data[features]
    y = model_data[target]

    # Add constant (intercept)
    X = sm.add_constant(X)

    # Fit OLS regression model
    model = sm.OLS(y, X).fit()
    residuals = model.resid

    # --- QQ Plot ---
    plt.figure(figsize=(6, 4))
    stats.probplot(residuals, dist="norm", plot=plt)
    plt.title(f'QQ Plot of Residuals - {target}')
    plt.grid(True)
    plt.show()

    # --- Residuals vs. Fitted Values Plot ---
    plt.figure(figsize=(6, 4))
    plt.scatter(model.fittedvalues, residuals, alpha=0.5)
    plt.axhline(0, color='red', linestyle='--')
    plt.title(f'Residuals vs Fitted - {target}')
    plt.xlabel('Fitted Values')
    plt.ylabel('Residuals')
    plt.grid(True)
    plt.show()

    # --- Residual Summary ---
    print("Residuals Summary:")
    print(f"  Mean: {residuals.mean():.4f}")
    print(f"  Std Dev: {residuals.std():.4f}")
    print(f"  Skewness: {residuals.skew():.4f}")
    print(f"  Kurtosis: {residuals.kurtosis():.4f}")

    # --- Shapiro-Wilk Test for Normality ---
    shapiro_test = stats.shapiro(residuals)
    print(f"  Shapiro-Wilk: Statistic={shapiro_test.statistic:.4f}, p-value={shapiro_test.pvalue:.4f}")
    if shapiro_test.pvalue > 0.05:
        print(" Residuals are approximately normal.")
    else:
        print(" Residuals deviate from normality.")

Modeling for: Life expectancy

Residuals Summary:
  Mean: -0.0000
  Std Dev: 8.2276
  Skewness: -1.0599
  Kurtosis: 1.9206
  Shapiro-Wilk: Statistic=0.9420, p-value=0.0000
 Residuals deviate from normality.

Modeling for: Cardiovascular diseases

/usr/local/lib/python3.11/dist-packages/scipy/stats/_axis_nan_policy.py:586: UserWarning: scipy.stats.shapiro: For N > 5000, computed p-value may not be accurate. Current N is 17504.
  res = hypotest_fun_out(*samples, **kwds)

Residuals Summary:
  Mean: 0.0001
  Std Dev: 120.4087
  Skewness: 4.6438
  Kurtosis: 59.1114
  Shapiro-Wilk: Statistic=0.3134, p-value=0.0000
 Residuals deviate from normality.

Modeling for: Diabetes

/usr/local/lib/python3.11/dist-packages/scipy/stats/_axis_nan_policy.py:586: UserWarning: scipy.stats.shapiro: For N > 5000, computed p-value may not be accurate. Current N is 17504.
  res = hypotest_fun_out(*samples, **kwds)

Residuals Summary:
  Mean: -0.0000
  Std Dev: 3.2707
  Skewness: 1.4892
  Kurtosis: 5.4749
  Shapiro-Wilk: Statistic=0.8798, p-value=0.0000
 Residuals deviate from normality.

/usr/local/lib/python3.11/dist-packages/scipy/stats/_axis_nan_policy.py:586: UserWarning: scipy.stats.shapiro: For N > 5000, computed p-value may not be accurate. Current N is 17504.
  res = hypotest_fun_out(*samples, **kwds)

# Histogram and Skewness Summary

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Replace this with your actual DataFrame
# df = pd.read_csv('your_dataset.csv')

# Identify numeric columns
numeric_cols = df.select_dtypes(include='number').columns

# Calculate skewness
skewness_summary = df[numeric_cols].skew().sort_values(ascending=False)
print("Skewness Summary:")
print(skewness_summary)

# Plot histogram and KDE for each numeric column
for col in numeric_cols:
    plt.figure(figsize=(10, 4))

    plt.subplot(1, 2, 1)
    sns.histplot(df[col].dropna(), bins=30, kde=False)
    plt.title(f'Histogram of {col}')

    plt.subplot(1, 2, 2)
    sns.kdeplot(df[col].dropna(), shade=True)
    plt.title(f'KDE Plot of {col}')

    plt.tight_layout()
    plt.show()

Skewness Summary:
Inflation                        75.489967
CPI                              25.637506
Cardiovascular diseases          10.419131
GDP                               8.488527
Sex ratio                         7.718123
Diabetes                          1.823593
Income                            1.618565
Unemployment Rate                 1.487679
Child mortality rate              1.458613
Incomplete tertiary education     1.154120
Median age                        0.899402
Gini coefficient                  0.820521
Cost of a healthy diet            0.642813
BMI (female)                      0.257175
BMI (male)                        0.065172
Year                             -0.002662
Life expectancy                  -0.691259
dtype: float64

/tmp/ipython-input-7-1776002204.py:27: FutureWarning: 

`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.

  sns.kdeplot(df[col].dropna(), shade=True)

/tmp/ipython-input-7-1776002204.py:27: FutureWarning: 

`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.

  sns.kdeplot(df[col].dropna(), shade=True)

/tmp/ipython-input-7-1776002204.py:27: FutureWarning: 

`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.

  sns.kdeplot(df[col].dropna(), shade=True)

/tmp/ipython-input-7-1776002204.py:27: FutureWarning: 

`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.

  sns.kdeplot(df[col].dropna(), shade=True)

/tmp/ipython-input-7-1776002204.py:27: FutureWarning: 

`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.

  sns.kdeplot(df[col].dropna(), shade=True)

/tmp/ipython-input-7-1776002204.py:27: FutureWarning: 

`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.

  sns.kdeplot(df[col].dropna(), shade=True)

/tmp/ipython-input-7-1776002204.py:27: FutureWarning: 

`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.

  sns.kdeplot(df[col].dropna(), shade=True)

/tmp/ipython-input-7-1776002204.py:27: FutureWarning: 

`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.

  sns.kdeplot(df[col].dropna(), shade=True)

/tmp/ipython-input-7-1776002204.py:27: FutureWarning: 

`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.

  sns.kdeplot(df[col].dropna(), shade=True)

# Boxplot

import seaborn as sns
import matplotlib.pyplot as plt

# Loop through all numeric columns to create boxplots
for col in df.select_dtypes(include='number').columns:
    # Get the data for the current numeric column
    column_data = df[col].dropna() # Drop NaN values to avoid potential issues with plotting

    # Check if there is enough data for plotting (at least one non-null value)
    if len(column_data) > 0:
        sns.boxplot(x=column_data)
        plt.title(f"Boxplot of {col}")
        plt.xlabel(col)
        plt.show()
    else:
        print(f"Not enough data to generate boxplot for column: {col}")

# Spearman Correlation matrix and heatmap

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np # Import numpy for np.number

# Compute correlation matrix - Select only numeric columns
corr_method = 'spearman'
# Select only numeric columns for correlation calculation
df_numeric = df.select_dtypes(include=np.number)
corr_matrix = df_numeric.corr(method=corr_method)

# Plot heatmap
plt.figure(figsize=(12, 10))
sns.heatmap(corr_matrix, annot=True, fmt=".2f", cmap='coolwarm', square=True)
plt.title(f'{corr_method.capitalize()} Correlation Heatmap')
plt.show()

# feature selection comparison
from sklearn.linear_model import Ridge
from sklearn.impute import SimpleImputer

def find_best_feature_count(X_df, y, max_features=None):
    import numpy as np
    import pandas as pd
    from sklearn.linear_model import LassoCV, LinearRegression
    from sklearn.feature_selection import RFE, SequentialFeatureSelector
    from sklearn.ensemble import RandomForestRegressor
    from sklearn.model_selection import TimeSeriesSplit
    from sklearn.metrics import mean_squared_error
    from sklearn.preprocessing import StandardScaler

    feature_names = X_df.columns.tolist()

    # 1. Impute missing values
    imputer = SimpleImputer(strategy='mean')  # or 'median', 'most_frequent'
    X_imputed = imputer.fit_transform(X_df)
    y_imputed = imputer.fit_transform(y.values.reshape(-1, 1)).ravel()

    # --- Scale X and y ---
    scaler = StandardScaler()
    X_scaled = scaler.fit_transform(X_imputed)

    y_imputed = y_imputed.reshape(-1, 1)
    y_scaler = StandardScaler()
    y_scaled = y_scaler.fit_transform(y_imputed).ravel()
    y_original = y_imputed.ravel()

      #y_scaled = y_scaler.fit_transform(y).ravel()
    #y_original = y.ravel()

    tscv = TimeSeriesSplit(n_splits=3)

    def rmse_on_original_scale(model, X_subset):
        y_preds, y_tests = [], []
        for train_idx, test_idx in tscv.split(X_subset):
            model.fit(X_subset[train_idx], y_scaled[train_idx])
            y_pred_scaled = model.predict(X_subset[test_idx])
            y_pred_original = y_scaler.inverse_transform(y_pred_scaled.reshape(-1, 1)).ravel()
            y_preds.extend(y_pred_original)
            y_tests.extend(y_original[test_idx])
        return np.sqrt(mean_squared_error(y_tests, y_preds))

    # --- Feature Selection ---
    max_features = min(max_features or 20, X_scaled.shape[1] - 1)
    lasso = LassoCV(cv=tscv, random_state=42).fit(X_scaled, y_scaled)
    lasso_coef = lasso.coef_
    rf_model = RandomForestRegressor(n_estimators=100, random_state=42)
    rf_model.fit(X_scaled, y_scaled)
    importances = rf_model.feature_importances_

    lasso_rmse_list, rfe_rmse_list, sfs_rmse_list, rf_rmse_list = [], [], [], []

    step = 2
    for n in range(1, max_features + 1, step):
        idx_lasso = np.argsort(np.abs(lasso_coef))[-n:]
        X_lasso = X_scaled[:, idx_lasso]
        lasso_rmse_list.append((n, rmse_on_original_scale(LinearRegression(), X_lasso)))

        try:
            rfe = RFE(LinearRegression(), n_features_to_select=n)
            X_rfe = rfe.fit_transform(X_scaled, y_scaled)
            rfe_rmse_list.append((n, rmse_on_original_scale(LinearRegression(), X_rfe)))
        except:
            rfe_rmse_list.append((n, np.nan))

        try:
            sfs = SequentialFeatureSelector(LinearRegression(), n_features_to_select=n, direction='forward', cv=tscv, n_jobs=-1)
            X_sfs = sfs.fit_transform(X_scaled, y_scaled)
            sfs_rmse_list.append((n, rmse_on_original_scale(LinearRegression(), X_sfs)))
        except:
            sfs_rmse_list.append((n, np.nan))

        idx_rf = np.argsort(importances)[-n:]
        X_rf = X_scaled[:, idx_rf]
        rf_rmse_list.append((n, rmse_on_original_scale(LinearRegression(), X_rf)))

    df_combined = (
        pd.DataFrame(lasso_rmse_list, columns=['n_features', 'LASSO_RMSE'])
        .merge(pd.DataFrame(rfe_rmse_list, columns=['n_features', 'RFE_RMSE']), on='n_features')
        .merge(pd.DataFrame(sfs_rmse_list, columns=['n_features', 'Forward_RMSE']), on='n_features')
        .merge(pd.DataFrame(rf_rmse_list, columns=['n_features', 'RF_RMSE']), on='n_features')
    )

    # --- Feature Names ---
    best_lasso_n = df_combined.loc[df_combined['LASSO_RMSE'].idxmin(), 'n_features']
    best_rfe_n = df_combined.loc[df_combined['RFE_RMSE'].idxmin(), 'n_features']
    best_sfs_n = df_combined.loc[df_combined['Forward_RMSE'].idxmin(), 'n_features']
    best_rf_n = df_combined.loc[df_combined['RF_RMSE'].idxmin(), 'n_features']

    lasso_features = [feature_names[i] for i in np.argsort(np.abs(lasso_coef))[-best_lasso_n:]]
    rfe = RFE(LinearRegression(), n_features_to_select=best_rfe_n).fit(X_scaled, y_scaled)
    rfe_features = [feature_names[i] for i, flag in enumerate(rfe.support_) if flag]
    sfs = SequentialFeatureSelector(LinearRegression(), n_features_to_select=best_sfs_n, direction='forward', cv=tscv).fit(X_scaled, y_scaled)
    sfs_features = [feature_names[i] for i, flag in enumerate(sfs.get_support()) if flag]
    rf_features = [feature_names[i] for i in np.argsort(importances)[-best_rf_n:]]

    best_methods = {
        'LASSO': {'n_features': best_lasso_n, 'rmse': df_combined.loc[df_combined['n_features'] == best_lasso_n, 'LASSO_RMSE'].values[0], 'features': lasso_features},
        'RFE': {'n_features': best_rfe_n, 'rmse': df_combined.loc[df_combined['n_features'] == best_rfe_n, 'RFE_RMSE'].values[0], 'features': rfe_features},
        'Forward': {'n_features': best_sfs_n, 'rmse': df_combined.loc[df_combined['n_features'] == best_sfs_n, 'Forward_RMSE'].values[0], 'features': sfs_features},
        'RandomForest': {'n_features': best_rf_n, 'rmse': df_combined.loc[df_combined['n_features'] == best_rf_n, 'RF_RMSE'].values[0], 'features': rf_features}
    }

    return df_combined, best_methods

import matplotlib.pyplot as plt

target_cols = ['Cardiovascular diseases', 'Diabetes', 'Life expectancy']
results = {}

for target in target_cols:
    lag_cols = [f'{target}_lag1', f'{target}_lag2']
    cols_to_drop = target_cols + [col for col in lag_cols if col in df_lagged.columns]
    X = df_lagged.drop(columns=cols_to_drop)
    y = df_lagged[target]

    print(f"\n🔍 Feature selection for target: {target}")
    df_combined, best_methods = find_best_feature_count(X, y)
    results[target] = {'df_combined': df_combined, 'best_methods': best_methods}

    for method, info in best_methods.items():
        print(f"\nMethod: {method}")
        print(f"Best number of features: {info['n_features']}")
        print(f"Best RMSE: {info['rmse']:.4f}")
        print(f"Selected features: {info['features']}")

    plt.figure(figsize=(10,6))
    plt.plot(df_combined['n_features'], df_combined['LASSO_RMSE'], label='LASSO', marker='o')
    plt.plot(df_combined['n_features'], df_combined['RFE_RMSE'], label='RFE', marker='s')
    plt.plot(df_combined['n_features'], df_combined['Forward_RMSE'], label='Forward', marker='^')
    plt.plot(df_combined['n_features'], df_combined['RF_RMSE'], label='Random Forest', marker='v')
    plt.xlabel('Number of Features')
    plt.ylabel('RMSE')
    plt.title(f'RMSE vs Number of Features for Target: {target}')
    plt.grid(True)
    plt.legend()
    plt.show()

🔍 Feature selection for target: Cardiovascular diseases

Method: LASSO
Best number of features: 3
Best RMSE: 144.6855
Selected features: ['BMI_avg_lag2', 'BMI_avg_lag3', 'GDP']

Method: RFE
Best number of features: 3
Best RMSE: 144.5087
Selected features: ['Income', 'GDP', 'BMI_avg']

Method: Forward
Best number of features: 11
Best RMSE: 145.4053
Selected features: ['Unemployment Rate', 'Incomplete tertiary education', 'GDP', 'Unemployment Rate_lag1', 'Unemployment Rate_lag2', 'Unemployment Rate_lag3', 'Incomplete tertiary education_lag1', 'Incomplete tertiary education_lag2', 'Incomplete tertiary education_lag3', 'GDP_lag1', 'lagged']

Method: RandomForest
Best number of features: 3
Best RMSE: 145.3185
Selected features: ['GDP_lag1', 'GDP', 'Incomplete tertiary education']

🔍 Feature selection for target: Diabetes

Method: LASSO
Best number of features: 11
Best RMSE: 3.6356
Selected features: ['Incomplete tertiary education_lag3', 'Sex ratio_lag3', 'Incomplete tertiary education', 'Income_lag3', 'CPI', 'Median age_lag3', 'Cost of a healthy diet', 'Income', 'GDP', 'BMI_avg', 'BMI_avg_lag3']

Method: RFE
Best number of features: 9
Best RMSE: 3.6424
Selected features: ['Cost of a healthy diet', 'Income', 'Incomplete tertiary education', 'GDP', 'CPI', 'BMI_avg', 'Income_lag3', 'Median age_lag3', 'BMI_avg_lag3']

Method: Forward
Best number of features: 17
Best RMSE: 3.6281
Selected features: ['Income', 'Inflation', 'Child mortality rate', 'Incomplete tertiary education', 'Sex ratio', 'GDP', 'BMI_avg', 'Cost of a healthy diet_lag2', 'Income_lag1', 'Income_lag2', 'Income_lag3', 'Inflation_lag3', 'Sex ratio_lag3', 'CPI_lag3', 'BMI_avg_lag1', 'BMI_avg_lag3', 'lagged']

Method: RandomForest
Best number of features: 17
Best RMSE: 3.6859
Selected features: ['CPI', 'Median age', 'Unemployment Rate_lag3', 'Inflation', 'Unemployment Rate_lag1', 'Median age_lag3', 'GDP_lag1', 'Cost of a healthy diet', 'Gini coefficient', 'Incomplete tertiary education', 'Unemployment Rate', 'GDP', 'Income', 'BMI_avg_lag2', 'BMI_avg_lag1', 'BMI_avg_lag3', 'BMI_avg']

🔍 Feature selection for target: Life expectancy

Method: LASSO
Best number of features: 5
Best RMSE: 3.5032
Selected features: ['Median age_lag3', 'Sex ratio', 'GDP', 'Child mortality rate_lag3', 'Child mortality rate']

Method: RFE
Best number of features: 7
Best RMSE: 3.5002
Selected features: ['Child mortality rate', 'Sex ratio', 'GDP', 'Median age', 'Child mortality rate_lag2', 'Child mortality rate_lag3', 'Median age_lag3']

Method: Forward
Best number of features: 17
Best RMSE: 3.4964
Selected features: ['Child mortality rate', 'Sex ratio', 'GDP', 'Median age', 'Child mortality rate_lag1', 'Child mortality rate_lag2', 'Child mortality rate_lag3', 'Sex ratio_lag1', 'Sex ratio_lag2', 'Sex ratio_lag3', 'GDP_lag1', 'GDP_lag2', 'GDP_lag3', 'Median age_lag2', 'BMI_avg_lag2', 'BMI_avg_lag3', 'lagged']

Method: RandomForest
Best number of features: 9
Best RMSE: 3.5058
Selected features: ['BMI_avg', 'GDP', 'Sex ratio', 'Income', 'Median age_lag3', 'Median age', 'Child mortality rate_lag2', 'Child mortality rate_lag3', 'Child mortality rate']

# Feature selection with R sq, MAPE, MSE

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.linear_model import LassoCV, LinearRegression
from sklearn.feature_selection import RFE, SequentialFeatureSelector
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import TimeSeriesSplit
from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error, r2_score
from sklearn.preprocessing import StandardScaler

# Move plot_metrics function definition to the beginning
def plot_metrics(df_combined, target_name):
     metrics = ['RMSE', 'MAPE', 'R2']
     methods = ['LASSO', 'RFE', 'Forward', 'RandomForest']

     for metric in metrics:
         plt.figure(figsize=(10,6))
         for method in methods:
             # Check if the metric column exists for the method before plotting
             metric_col = f'{method}_{metric}'
             if metric_col in df_combined.columns:
                 plt.plot(df_combined['n_features'], df_combined[metric_col], label=method, marker='o')
             else:
                 print(f"Warning: Metric column '{metric_col}' not found in DataFrame for plotting.")

         plt.title(f'{metric} vs Number of Features ({target_name})')
         plt.xlabel('Number of Features')
         plt.ylabel(metric)
         plt.legend()
         plt.grid(True)
         plt.show()


def evaluate_model(model, X_subset, y_scaled, y_original, y_scaler, tscv):
    y_preds, y_tests = [], []
    # Ensure X_subset and y_scaled have the same index for splitting
    # Convert X_subset to DataFrame if it's numpy array to use index for splitting
    if not isinstance(X_subset, pd.DataFrame):
        # Assuming X_subset corresponds to the same rows as y_scaled
        X_subset_df = pd.DataFrame(X_subset, index=pd.Series(y_scaled).index)
    else:
        X_subset_df = X_subset

    for train_idx, test_idx in tscv.split(X_subset_df): # Use X_subset_df for splitting
        # Select data using indices from the split
        X_train, X_test = X_subset_df.iloc[train_idx], X_subset_df.iloc[test_idx]
        y_train_scaled, y_test_scaled = y_scaled[train_idx], y_scaled[test_idx]
        y_test_original = y_original[test_idx] # Select original y for test set


        # Ensure there's data in train and test sets for the current fold
        if len(X_train) > 0 and len(X_test) > 0:
            try:
                model.fit(X_train, y_train_scaled)
                y_pred_scaled = model.predict(X_test)
                y_pred_original = y_scaler.inverse_transform(y_pred_scaled.reshape(-1, 1)).ravel()
                y_preds.extend(y_pred_original)
                y_tests.extend(y_test_original)
            except Exception as e:
                 print(f"Error during model fitting or prediction in a fold: {e}")
                 # Extend with NaNs or skip if error occurs in a fold
                 y_preds.extend([np.nan] * len(y_test_original))
                 y_tests.extend(y_test_original) # Still add the test actuals to keep lists aligned

    # Calculate metrics only if y_tests and y_preds are not empty and don't contain NaNs/Infs
    y_tests_cleaned = np.array(y_tests)
    y_preds_cleaned = np.array(y_preds)

    # Remove pairs where either actual or prediction is NaN/Inf
    valid_indices = np.isfinite(y_tests_cleaned) & np.isfinite(y_preds_cleaned)
    y_tests_cleaned = y_tests_cleaned[valid_indices]
    y_preds_cleaned = y_preds_cleaned[valid_indices]

    if len(y_tests_cleaned) > 0:
        rmse = np.sqrt(mean_squared_error(y_tests_cleaned, y_preds_cleaned))
        mape = mean_absolute_percentage_error(y_tests_cleaned, y_preds_cleaned)
        r2 = r2_score(y_tests_cleaned, y_preds_cleaned)
    else:
        # Return NaN if no valid data points for metric calculation
        rmse, mape, r2 = np.nan, np.nan, np.nan

    return rmse, mape, r2

def find_best_features_with_metrics(X_df, y, max_features=None):
    # Ensure X_df has a proper index for splitting in evaluate_model
    if not isinstance(X_df.index, pd.MultiIndex):
        # Assuming X_df came from df_lagged which has MultiIndex, try to restore it
        # This might require passing the original index or ensuring X_df retains it
        # For robustness, let's assume X_df needs an index for splitting
        # A simpler approach might be to reset index in evaluate_model if it's numpy array
        pass # Let's handle index in evaluate_model as it receives X_subset

    X_scaler = StandardScaler()
    # Fit scaler on X_df values, but keep X_df as DataFrame to retain index
    X_scaled_values = X_scaler.fit_transform(X_df.values)
    X_scaled_df = pd.DataFrame(X_scaled_values, columns=X_df.columns, index=X_df.index) # Recreate DataFrame with index

    feature_names = X_scaled_df.columns.tolist()

    y = y.values.reshape(-1, 1) # y is already a Series from df_lagged, convert to numpy array
    y_original = y.ravel() # Keep original y values as numpy array

    y_scaler = StandardScaler()
    y_scaled = y_scaler.fit_transform(y).ravel() # Scale y


    tscv = TimeSeriesSplit(n_splits=5)
    max_features = min(max_features or 30, X_scaled_df.shape[1]) # Max features up to total features

    # Handle case where there are no features
    if X_scaled_df.shape[1] == 0:
        print("No features available in X_df. Skipping feature selection.")
        return pd.DataFrame(), {'LASSO': {'n_features': 0, 'rmse': np.nan, 'features': []},
                                'RFE': {'n_features': 0, 'rmse': np.nan, 'features': []},
                                'Forward': {'n_features': 0, 'rmse': np.nan, 'features': []},
                                'RandomForest': {'n_features': 0, 'rmse': np.nan, 'features': []}}


    lasso = LassoCV(cv=tscv, random_state=42).fit(X_scaled_df, y_scaled)
    lasso_coef = lasso.coef_
    # Ensure Random Forest is fitted on X_scaled_df (DataFrame)
    rf_model = RandomForestRegressor(n_estimators=100, random_state=42).fit(X_scaled_df, y_scaled)
    importances = rf_model.feature_importances_

    results = {'LASSO': [], 'RFE': [], 'Forward': [], 'RandomForest': []}

    # Max features for loop should be <= total features
    max_loop_features = min(max_features, X_scaled_df.shape[1])


    for n in range(1, max_loop_features + 1):
        # LASSO
        idx = np.argsort(np.abs(lasso_coef))[-n:]
        # Select columns using index from X_scaled_df
        X_subset_lasso = X_scaled_df.iloc[:, idx]
        # Pass DataFrame to evaluate_model
        results['LASSO'].append((n, *evaluate_model(LinearRegression(), X_subset_lasso, y_scaled, y_original, y_scaler, tscv)))

        # RFE
        try:
            # RFE requires n_features_to_select <= n_features
            if n <= X_scaled_df.shape[1]:
                rfe = RFE(LinearRegression(), n_features_to_select=n)
                # Fit on X_scaled_df (DataFrame) and get transformed numpy array
                X_subset_rfe_np = rfe.fit_transform(X_scaled_df, y_scaled)
                # Pass numpy array to evaluate_model - evaluate_model handles conversion to DataFrame for splitting
                results['RFE'].append((n, *evaluate_model(LinearRegression(), X_subset_rfe_np, y_scaled, y_original, y_scaler, tscv)))
            else:
                 results['RFE'].append((n, np.nan, np.nan, np.nan))

        except Exception as e:
             print(f"RFE failed for n={n}: {e}")
             results['RFE'].append((n, np.nan, np.nan, np.nan))


        # Forward
        try:
            # SFS requires k_features <= n_features
            if n <= X_scaled_df.shape[1]:
                # Use X_scaled_df (DataFrame) for SFS fit
                sfs = SequentialFeatureSelector(LinearRegression(), n_features_to_select=n, direction='forward', cv=tscv, n_jobs=-1)
                 # Fit on X_scaled_df (DataFrame) and get transformed numpy array
                X_subset_sfs_np = sfs.fit_transform(X_scaled_df, y_scaled)
                # Pass numpy array to evaluate_model
                results['Forward'].append((n, *evaluate_model(LinearRegression(), X_subset_sfs_np, y_scaled, y_original, y_scaler, tscv)))
            else:
                results['Forward'].append((n, np.nan, np.nan, np.nan))
        except Exception as e:
            print(f"Forward Selection failed for n={n}: {e}")
            results['Forward'].append((n, np.nan, np.nan, np.nan))


        # RF Importance
        idx = np.argsort(importances)[-n:]
        # Select columns using index from X_scaled_df
        X_subset_rf = X_scaled_df.iloc[:, idx]
        # Pass DataFrame to evaluate_model
        results['RandomForest'].append((n, *evaluate_model(LinearRegression(), X_subset_rf, y_scaled, y_original, y_scaler, tscv)))

    # Build metrics DataFrame
    dfs = []
    for method, vals in results.items():
        df = pd.DataFrame(vals, columns=['n_features', f'{method}_RMSE', f'{method}_MAPE', f'{method}_R2'])
        dfs.append(df)

    df_combined = dfs[0]
    for df in dfs[1:]:
        df_combined = df_combined.merge(df, on='n_features', how='outer')

    return df_combined

# Assuming df_lagged is available and contains the data with lags
# Assuming target_cols is defined

target_cols = ['Life expectancy', 'Cardiovascular diseases', 'Diabetes']
results = {}

for target in target_cols:
    # Ensure df_lagged is available and contains the target column
    if 'df_lagged' in locals() and target in df_lagged.columns:
        lag_cols = [f'{target}_lag1', f'{target}_lag2']
        # Ensure we only try to drop columns that exist in df_lagged
        cols_to_drop = [target] + [col for col in lag_cols if col in df_lagged.columns]

        # Select features for X - drop target(s) and their lags
        X = df_lagged.drop(columns=cols_to_drop)
        # Select the current target variable and drop NaNs
        y = df_lagged[target].dropna()

        # Align X with the cleaned y by index
        X = X.loc[y.index]


        # Ensure X is not empty after aligning with y
        if X.empty:
            print(f"No valid data points after dropping NaNs for target: {target}. Skipping evaluation.")
            results[target] = pd.DataFrame() # Store an empty DataFrame
            continue


        print(f"\n🔍 Evaluating for target: {target}")
        # Pass X as a DataFrame and y as a Series (without NaNs)
        df_metrics = find_best_features_with_metrics(X, y)
        results[target] = df_metrics

        # Plot metrics for the current target only if df_metrics is not empty
        if not df_metrics.empty:
             plot_metrics(df_metrics, target)
        else:
             print(f"No metrics to plot for target: {target}.")


    else:
        print(f"df_lagged or target column '{target}' not found. Skipping evaluation for this target.")

🔍 Evaluating for target: Life expectancy

🔍 Evaluating for target: Cardiovascular diseases

/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:681: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.3344987559357833, tolerance: 1.2543280301043949
  model = cd_fast.enet_coordinate_descent_gram(
/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:681: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.3041213861906726, tolerance: 1.2543280301043949
  model = cd_fast.enet_coordinate_descent_gram(
/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:681: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.2939728821220342, tolerance: 1.2543280301043949
  model = cd_fast.enet_coordinate_descent_gram(
/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:681: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.291560398247384, tolerance: 1.2543280301043949
  model = cd_fast.enet_coordinate_descent_gram(
/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:681: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.2833807594306563, tolerance: 1.2543280301043949
  model = cd_fast.enet_coordinate_descent_gram(
/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:681: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.2738431218404003, tolerance: 1.2543280301043949
  model = cd_fast.enet_coordinate_descent_gram(
/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:681: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.263577498113591, tolerance: 1.2543280301043949
  model = cd_fast.enet_coordinate_descent_gram(
/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:681: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.703431344112687, tolerance: 1.5837940758962923
  model = cd_fast.enet_coordinate_descent_gram(
/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:681: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.6969510646304116, tolerance: 1.5837940758962923
  model = cd_fast.enet_coordinate_descent_gram(
/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:681: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.6853641250145301, tolerance: 1.5837940758962923
  model = cd_fast.enet_coordinate_descent_gram(
/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:681: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.6726185397310473, tolerance: 1.5837940758962923
  model = cd_fast.enet_coordinate_descent_gram(
/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:681: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.6593221832208656, tolerance: 1.5837940758962923
  model = cd_fast.enet_coordinate_descent_gram(
/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:681: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.6457220310112461, tolerance: 1.5837940758962923
  model = cd_fast.enet_coordinate_descent_gram(
/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:681: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.631997326414421, tolerance: 1.5837940758962923
  model = cd_fast.enet_coordinate_descent_gram(
/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:681: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.618292309503886, tolerance: 1.5837940758962923
  model = cd_fast.enet_coordinate_descent_gram(
/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_coordinate_descent.py:681: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.6047235924033885, tolerance: 1.5837940758962923
  model = cd_fast.enet_coordinate_descent_gram(

🔍 Evaluating for target: Diabetes

## The best Feature Selection with different metrics TABLE -  REVISED - Seed not fixed

# Install tabulate if needed
!pip install tabulate

from sklearn.linear_model import Ridge, LassoCV
from sklearn.ensemble import RandomForestRegressor
from sklearn.feature_selection import SequentialFeatureSelector, RFE
from sklearn.model_selection import TimeSeriesSplit
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
from sklearn.preprocessing import StandardScaler
from tabulate import tabulate
import pandas as pd
import numpy as np
from sklearn.impute import SimpleImputer # Import Imputer

# Main function to calculate metrics for a given set of features
def calculate_metrics_for_features(X_df, y, feature_indices):
    # Impute missing values in X_df
    imputer = SimpleImputer(strategy='mean')
    X_df_imputed = pd.DataFrame(imputer.fit_transform(X_df), columns=X_df.columns, index=X_df.index)

    scaler = StandardScaler()
    X_scaled = scaler.fit_transform(X_df_imputed) # Use imputed data here
    y = y.values.reshape(-1, 1)
    y_scaler = StandardScaler().fit(y)
    y_scaled = y_scaler.transform(y).ravel()
    y_original = y.ravel()
    tscv = TimeSeriesSplit(n_splits=3)

    X_subset = X_scaled[:, feature_indices]

    y_preds, y_tests = [], []
    for train_idx, test_idx in tscv.split(X_subset):
        model = Ridge()
        model.fit(X_subset[train_idx], y_scaled[train_idx])
        pred = model.predict(X_subset[test_idx])
        y_pred = y_scaler.inverse_transform(pred.reshape(-1, 1)).ravel()
        y_preds.extend(y_pred)
        y_tests.extend(y_original[test_idx])

    return (
        np.sqrt(mean_squared_error(y_tests, y_preds)),
        mean_absolute_error(y_tests, y_preds),
        r2_score(y_tests, y_preds)
    )

# Extract Best Results per Method from the 'results' dictionary
def extract_best_per_method(results_dict, X_data_for_targets, y_data_for_targets):
    summary = []
    for target, target_results in results_dict.items():
        df_combined = target_results['df_combined']
        best_methods_info = target_results['best_methods']
        X_target = X_data_for_targets[target] # Get the correct X for this target
        y_target = y_data_for_targets[target] # Get the correct y for this target


        for method, info in best_methods_info.items():
            n_features = info['n_features']
            selected_feature_names = info['features']

            # Get the indices of the selected features from the X_target DataFrame columns
            try:
                # Ensure selected_feature_names are in the columns of X_target
                valid_selected_features = [col for col in selected_feature_names if col in X_target.columns]
                feature_indices = [X_target.columns.get_loc(col) for col in valid_selected_features]

            except KeyError as e:
                 print(f"Error: Feature '{e}' not found in original DataFrame columns for target {target}, method {method}. Skipping.")
                 continue # Skip this combination if features are not found


            if n_features > 0 and feature_indices:
                 # Calculate metrics using the selected features and the correct X_target and y_target
                 # Pass the subset of X_target using the valid_selected_features column names
                 rmse, mae, r2 = calculate_metrics_for_features(X_target[valid_selected_features], y_target, list(range(len(valid_selected_features)))) # Pass indices relative to the subset


                 summary.append({
                     'Target': target,
                     'Method': method,
                     'n_features': len(valid_selected_features), # Use the count of valid features
                     'RMSE': round(rmse, 2),
                     'MAE': round(mae, 2),
                     'R²': round(r2, 4)
                 })
            elif n_features == 0:
                 # Handle case with 0 features if necessary, although typically we select at least 1
                 summary.append({
                     'Target': target,
                     'Method': method,
                     'n_features': 0,
                     'RMSE': np.nan, # Or a baseline metric if applicable
                     'MAE': np.nan,
                     'R²': np.nan
                 })


    return pd.DataFrame(summary)

# Assuming df_lagged is available from previous steps
# Prepare the X and y dataframes for each target as they were used in the feature selection loop
target_cols = ['Life expectancy', 'Cardiovascular diseases', 'Diabetes']
X_data_for_targets = {}
y_data_for_targets = {}

if 'df_lagged' in locals():
    for target in target_cols:
        if target in df_lagged.columns:
            lag_cols = [f'{target}_lag1', f'{target}_lag2']
            cols_to_drop = [target] + [col for col in lag_cols if col in df_lagged.columns]
            X = df_lagged.drop(columns=cols_to_drop)
            y = df_lagged[target].dropna() # Use the y with NaNs dropped as in the previous cell

            # Align X with the cleaned y by index
            X = X.loc[y.index]

            X_data_for_targets[target] = X
            y_data_for_targets[target] = y
        else:
            print(f"Target column '{target}' not found in df_lagged. Cannot prepare data for this target.")

# Extract Best Results per Method
# Use the 'results' dictionary generated from the previous cell's execution and the prepared X and y data
if 'results' in locals() and results and X_data_for_targets and y_data_for_targets:
    best_performance_df = extract_best_per_method(results, X_data_for_targets, y_data_for_targets)

    # Print Final Table
    if not best_performance_df.empty:
        print("\nBest Performance per Method\n")
        print(tabulate(best_performance_df, headers='keys', tablefmt='fancy_grid', showindex=False))
    else:
        print("\nNo best performance results to display.")
else:
    print("\n'results' dictionary, X_data_for_targets, or y_data_for_targets not found or is empty. Please run the feature selection cell first and ensure data is prepared correctly.")

# export and download file
best_performance_df.to_csv("best_feature_selection_summary.csv", index=False)

from google.colab import files
files.download("best_feature_selection_summary.csv")

Requirement already satisfied: tabulate in /usr/local/lib/python3.11/dist-packages (0.9.0)

Best Performance per Method

╒═════════════════════════╤══════════════╤══════════════╤════════╤═══════╤═════════╕
│ Target                  │ Method       │   n_features │   RMSE │   MAE │      R² │
╞═════════════════════════╪══════════════╪══════════════╪════════╪═══════╪═════════╡
│ Cardiovascular diseases │ LASSO        │            3 │ 144.69 │ 37.6  │  0.0043 │
├─────────────────────────┼──────────────┼──────────────┼────────┼───────┼─────────┤
│ Cardiovascular diseases │ RFE          │            3 │ 144.51 │ 38.52 │  0.0067 │
├─────────────────────────┼──────────────┼──────────────┼────────┼───────┼─────────┤
│ Cardiovascular diseases │ Forward      │           11 │ 145.41 │ 37.95 │ -0.0056 │
├─────────────────────────┼──────────────┼──────────────┼────────┼───────┼─────────┤
│ Cardiovascular diseases │ RandomForest │            3 │ 145.32 │ 37.89 │ -0.0044 │
├─────────────────────────┼──────────────┼──────────────┼────────┼───────┼─────────┤
│ Diabetes                │ LASSO        │           11 │   3.64 │  2.6  │  0.4649 │
├─────────────────────────┼──────────────┼──────────────┼────────┼───────┼─────────┤
│ Diabetes                │ RFE          │            9 │   3.64 │  2.6  │  0.4629 │
├─────────────────────────┼──────────────┼──────────────┼────────┼───────┼─────────┤
│ Diabetes                │ Forward      │           17 │   3.63 │  2.58 │  0.4671 │
├─────────────────────────┼──────────────┼──────────────┼────────┼───────┼─────────┤
│ Diabetes                │ RandomForest │           17 │   3.69 │  2.62 │  0.45   │
├─────────────────────────┼──────────────┼──────────────┼────────┼───────┼─────────┤
│ Life expectancy         │ LASSO        │            5 │   3.5  │  2.69 │  0.9133 │
├─────────────────────────┼──────────────┼──────────────┼────────┼───────┼─────────┤
│ Life expectancy         │ RFE          │            7 │   3.5  │  2.68 │  0.9134 │
├─────────────────────────┼──────────────┼──────────────┼────────┼───────┼─────────┤
│ Life expectancy         │ Forward      │           17 │   3.5  │  2.68 │  0.9136 │
├─────────────────────────┼──────────────┼──────────────┼────────┼───────┼─────────┤
│ Life expectancy         │ RandomForest │            9 │   3.51 │  2.68 │  0.9132 │
╘═════════════════════════╧══════════════╧══════════════╧════════╧═══════╧═════════╛

# Feature Importance Table - REVISED

from sklearn.linear_model import LinearRegression, LassoCV
from sklearn.ensemble import RandomForestRegressor
from sklearn.feature_selection import RFE
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import StandardScaler
import pandas as pd
import numpy as np

# === Setup
targets = ['Cardiovascular diseases', 'Diabetes', 'Life expectancy']
methods = ['Forward', 'RFE', 'LASSO', 'Random Forest']
all_features = [
    'Child mortality rate', 'Child mortality rate_lag3', 'GDP', 'Sex ratio_lag1', 'Unemployment Rate_lag1',
    'Cost of a healthy diet', 'Child mortality rate_lag2', 'Sex ratio', 'Sex ratio_lag2', 'Unemployment Rate',
    'Unemployment Rate_lag3', 'Unemployment Rate_lag2', 'Sex ratio_lag3', 'Cost of a healthy diet_lag1',
    'Cost of a healthy diet_lag2', 'Cost of a healthy diet_lag3', 'GDP_lag1','BMI_avg_lag3', 'Income',
    'GDP_lag3', 'Median age_lag3', 'Income_lag3', 'Income_lag2', 'Income_lag1', 'GDP_lag2', 'BMI_avg_lag2',
    'BMI_avg_lag1', 'BMI_avg','Incomplete tertiary education'
]

# === Initialize importance table
multi_method_importance = pd.DataFrame(index=all_features,
                                       columns=pd.MultiIndex.from_product([targets, methods]),
                                       dtype=float).fillna(0.0)

# === Function: Standardization and prep
def prepare_data(target, features):
    valid_features = [f for f in features if f in df_lagged.columns]
    df_temp = df_lagged[valid_features + [target]].dropna()
    X = df_temp[valid_features]
    y = df_temp[target]
    X_scaled = pd.DataFrame(StandardScaler().fit_transform(X), columns=X.columns)
    return X_scaled, y, valid_features

# === FORWARD SELECTION
def run_forward(X, y, valid_features, max_features):
    selected = []
    remaining = valid_features.copy()
    for _ in range(min(max_features, len(remaining))):
        scores = {}
        for f in remaining:
            trial = selected + [f]
            model = LinearRegression()
            neg_mse = cross_val_score(model, X[trial], y,
                                      scoring='neg_mean_squared_error', cv=5)
            rmse = np.mean(np.sqrt(-neg_mse))
            scores[f] = rmse
        best_feature = min(scores, key=scores.get)
        selected.append(best_feature)
        remaining.remove(best_feature)
    for f in selected:
        multi_method_importance.loc[f, (target, 'Forward')] = 1

# === RFE
def run_rfe(X, y, valid_features, num_features):
    model = LinearRegression()
    selector = RFE(model, n_features_to_select=num_features)
    selector = selector.fit(X, y)
    for f, support in zip(valid_features, selector.support_):
        if support:
            multi_method_importance.loc[f, (target, 'RFE')] = 1

# === LASSO
def run_lasso(X, y):
    model = LassoCV(cv=5, random_state=42)
    model.fit(X, y)
    for i, f in enumerate(X.columns):
        multi_method_importance.loc[f, (target, 'LASSO')] = round(abs(model.coef_[i]), 4)

# === RANDOM FOREST
def run_rf(X, y):
    rf = RandomForestRegressor(n_estimators=100, random_state=42)
    rf.fit(X, y)
    importances = rf.feature_importances_
    for i, f in enumerate(X.columns):
        multi_method_importance.loc[f, (target, 'Random Forest')] = round(importances[i], 4)

# === Run loop for all targets
for target in targets:
    if target not in df_lagged.columns:
        print(f"⚠️ Skipping {target} — not found in dataset.")
        continue

    X_scaled, y, valid = prepare_data(target, all_features)

    run_forward(X_scaled, y, valid, max_features=17 if target != 'Cardiovascular diseases' else 3)
    run_rfe(X_scaled, y, valid, num_features=3)
    run_lasso(X_scaled, y)
    run_rf(X_scaled, y)

# === Display styled table
styled_multi_table = multi_method_importance.style \
    .set_caption("📊 Multi-Method Feature Importance Table") \
    .format(precision=4) \
    .set_table_styles([
        {'selector': 'table', 'props': [('border-collapse', 'collapse'),
                                        ('border', '1px solid black')]},
        {'selector': 'th, td', 'props': [('border', '1px solid black'), ('padding', '4px')]}
    ])

# Display multi table

# Summary table combining the feature importance score with descending order for each target
for target in targets:
    col_forward = (target, 'Forward')
    col_rfe = (target, 'RFE')
    col_lasso = (target, 'LASSO')
    col_rf = (target, 'Random Forest')

    combined_name = (target, 'Combined')
    multi_method_importance[combined_name] = (
        multi_method_importance[col_forward].fillna(0) +
        multi_method_importance[col_rfe].fillna(0) +
        multi_method_importance[col_lasso].fillna(0) +
        multi_method_importance[col_rf].fillna(0)
    )

# === Reorder for display: sort by combined score for each target
for target in targets:
    sort_col = (target, 'Combined')
    sorted_features = multi_method_importance.sort_values(by=sort_col, ascending=False).index
    multi_method_importance = multi_method_importance.loc[sorted_features]

# === Display enhanced table
styled_combined_table = multi_method_importance.style \
    .set_caption("⭐ Enhanced Feature Importance Comparison (4 Methods + Combined)") \
    .format(precision=4) \
    .set_table_styles([
        {'selector': 'table', 'props': [('border-collapse', 'collapse'), ('border', '1px solid black')]},
        {'selector': 'th, td', 'props': [('border', '1px solid black'), ('padding', '5px')]}
    ])

display(styled_combined_table)

# Save the file
multi_method_importance.to_csv("enhanced_feature_importance_comparison.csv", index=True)

# Download the file (include full path)
from google.colab import files
files.download('enhanced_feature_importance_comparison.csv')

# Bar Plot top features per target based on combined score

# === Custom color map
custom_colors = {
    'Cardiovascular diseases': 'mediumseagreen',
    'Diabetes': 'darkorange',
    'Life expectancy': 'cornflowerblue'
}
targets = ['Cardiovascular diseases', 'Diabetes', 'Life expectancy']
# === Plot top features per target with color
top_n = 10  # adjust as needed
for target in targets:
    combined_col = (target, 'Combined')
    df_top = multi_method_importance[[combined_col]].copy()
    df_top.columns = ['Combined Score']
    df_top = df_top.sort_values(by='Combined Score', ascending=False).head(top_n)

    plt.figure(figsize=(10, 6))
    sns.barplot(x='Combined Score', y=df_top.index, data=df_top, color=custom_colors[target])
    plt.title(f"Top {top_n} Features for {target} (Combined Score)", fontsize=14)
    plt.xlabel("Combined Importance")
    plt.ylabel("Feature")
    plt.tight_layout()
    plt.show()

# ACF and PACF plot

from statsmodels.graphics.tsaplots import plot_acf, plot_pacf

# Assuming df_lagged is your dataset and contains time-series data
target_cols = ['Life expectancy', 'Cardiovascular diseases', 'Diabetes']

for target in target_cols:
    series = df_lagged[target].dropna()

    fig, ax = plt.subplots(2, 1, figsize=(10, 8), sharex=True)
    fig.suptitle(f'ACF and PACF for {target}', fontsize=16)

    plot_acf(series, lags=40, ax=ax[0])
    ax[0].set_title(f'Autocorrelation (ACF) - {target}')
    ax[0].set_ylabel('ACF')

    plot_pacf(series, lags=40, ax=ax[1], method='ywm')
    ax[1].set_title(f'Partial Autocorrelation (PACF) - {target}')
    ax[1].set_ylabel('PACF')

    plt.tight_layout(rect=[0, 0.03, 1, 0.95])
    plt.show()

# Residual Diagnostics - Test and Summary Table -  REVISED

import statsmodels.api as sm
from statsmodels.stats.diagnostic import het_breuschpagan
from statsmodels.stats.diagnostic import acorr_ljungbox
import pandas as pd
import numpy as np
from tabulate import tabulate
from google.colab import files

# === Function: Residual Diagnostics for one target ===
def residual_diagnostics(X, y):
    data = pd.concat([X, y], axis=1).dropna()
    X_cleaned = data[X.columns]
    y_cleaned = data[y.name]

    X_const = sm.add_constant(X_cleaned)
    model = sm.OLS(y_cleaned, X_const).fit()
    residuals = model.resid

    # Breusch-Pagan Test
    bp_test = het_breuschpagan(residuals, X_const.loc[residuals.index])
    bp_labels = ['LM stat', 'BP p-value', 'BP f-value', 'BP f p-value']
    bp_results = dict(zip(bp_labels, bp_test))

    # Ljung-Box Test
    if len(residuals) > 10:
        lb_test = acorr_ljungbox(residuals, lags=[10], return_df=True)
        lb_pvalue = lb_test['lb_pvalue'].iloc[0]
    else:
        lb_pvalue = "Insufficient data (n < 10)"

    return bp_results, lb_pvalue, residuals

# === Setup
target_cols = ['Life expectancy', 'Cardiovascular diseases', 'Diabetes']
diagnostics_summary = []

if 'df_lagged' in locals():
    for target_col in target_cols:
        print(f"\n Running diagnostics for: {target_col}")

        if target_col not in df_lagged.columns:
            print(f" Skipping {target_col} — not found in df_lagged")
            continue

        y = df_lagged[target_col]
        X = df_lagged.drop(columns=target_cols, errors='ignore')

        data = pd.concat([X, y], axis=1).dropna()
        if data.empty:
            print(" Not enough data after dropping NaNs")
            continue

        bp_results, lb_pvalue, residuals = residual_diagnostics(X, y)
        diagnostics_summary.append({
            "Target": target_col,
            "Breusch-Pagan LM stat": round(bp_results['LM stat'], 4),
            "BP p-value": round(bp_results['BP p-value'], 4),
            "BP f-value": round(bp_results['BP f-value'], 4),
            "BP f p-value": round(bp_results['BP f p-value'], 4),
            "Ljung-Box p-value (lag=10)": lb_pvalue,
            "Residual Mean": round(residuals.mean(), 4),
            "Residual Variance": round(residuals.var(), 4)
        })

    # === Create summary table
    diagnostics_df = pd.DataFrame(diagnostics_summary)

    # Print as fancy table
    print("\n📋 Residual Diagnostics Summary:")
    print(tabulate(diagnostics_df, headers='keys', tablefmt='fancy_grid', showindex=False))

    # === Export to CSV
    filename = "residual_diagnostics_summary.csv"
    diagnostics_df.to_csv(filename, index=False)

    # Download the file (include full path)
    from google.colab import files
    files.download(filename)

else:
    print("❗ df_lagged is not defined. Please run your preprocessing cell first.")

 Running diagnostics for: Life expectancy

 Running diagnostics for: Cardiovascular diseases

 Running diagnostics for: Diabetes

📋 Residual Diagnostics Summary:
╒═════════════════════════╤═════════════════════════╤══════════════╤══════════════╤════════════════╤══════════════════════════════╤═════════════════╤═════════════════════╕
│ Target                  │   Breusch-Pagan LM stat │   BP p-value │   BP f-value │   BP f p-value │   Ljung-Box p-value (lag=10) │   Residual Mean │   Residual Variance │
╞═════════════════════════╪═════════════════════════╪══════════════╪══════════════╪════════════════╪══════════════════════════════╪═════════════════╪═════════════════════╡
│ Life expectancy         │                1054.96  │            0 │      23.3712 │              0 │                            0 │               0 │             11.3211 │
├─────────────────────────┼─────────────────────────┼──────────────┼──────────────┼────────────────┼──────────────────────────────┼─────────────────┼─────────────────────┤
│ Cardiovascular diseases │                 423.071 │            0 │       9.0137 │              0 │                            0 │               0 │          19607.4    │
├─────────────────────────┼─────────────────────────┼──────────────┼──────────────┼────────────────┼──────────────────────────────┼─────────────────┼─────────────────────┤
│ Diabetes                │                1517.67  │            0 │      34.6315 │              0 │                            0 │               0 │              9.8512 │
╘═════════════════════════╧═════════════════════════╧══════════════╧══════════════╧════════════════╧══════════════════════════════╧═════════════════╧═════════════════════╛

# HAC REVISED
import statsmodels.api as sm
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Target columns
target_cols = ['Life expectancy', 'Cardiovascular diseases', 'Diabetes']

# Iterate through each target
for target in target_cols:
    print(f"\n=== Newey-West Adjusted OLS Results for: {target} ===")

    try:
        # Define target and predictors
        y = df_lagged[target]
        all_target_cols_in_df = [col for col in target_cols if col in df_lagged.columns]
        X = df_lagged.drop(columns=all_target_cols_in_df, errors='ignore')

        # Combine and clean
        data = pd.concat([X, y], axis=1).dropna()
        X_cleaned = data[X.columns]
        y_cleaned = data[y.name]

        # Add constant term
        X_const = sm.add_constant(X_cleaned)

        # Fit OLS
        model = sm.OLS(y_cleaned, X_const).fit()

        # Newey-West HAC adjustment
        nobs = len(y_cleaned)
        maxlags = min(5, nobs - 1)
        nw_model = model.get_robustcov_results(cov_type='HAC', maxlags=maxlags)

        # Print summary
        print(nw_model.summary())

        # Residual analysis
        residuals = nw_model.resid
        print(f"\n📊 Residuals Summary for '{target}':")
        print(pd.Series(residuals).describe())

        if len(residuals) > 0 and not np.all(residuals == 0):
            plt.figure(figsize=(10, 4))
            plt.plot(range(len(residuals)), residuals, color='darkblue', linewidth=1)
            plt.axhline(0, color='gray', linestyle='--')
            plt.title(f"Residuals Over Time — {target}", fontsize=14)
            plt.xlabel("Observation Index")
            plt.ylabel("Residual")
            plt.grid(True)
            plt.tight_layout()
            plt.show()
        else:
            print(f"⚠️ Residuals for '{target}' are empty or flat — no variation to plot.")

    except Exception as e:
        print(f"❌ Could not fit HAC model or plot residuals for {target}: {e}")

# export and download file
best_performance_df.to_csv("best_feature_selection_summary.csv", index=False)

from google.colab import files
files.download("best_feature_selection_summary.csv")

=== Newey-West Adjusted OLS Results for: Life expectancy ===
                            OLS Regression Results                            
==============================================================================
Dep. Variable:        Life expectancy   R-squared:                       0.918
Model:                            OLS   Adj. R-squared:                  0.917
Method:                 Least Squares   F-statistic:                     1.278
Date:                Thu, 24 Jul 2025   Prob (F-statistic):              0.276
Time:                        19:55:10   Log-Likelihood:                -44559.
No. Observations:               16928   AIC:                         8.922e+04
Df Residuals:                   16879   BIC:                         8.959e+04
Df Model:                          48                                         
Covariance Type:                  HAC                                         
======================================================================================================
                                         coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------------------------------
const                              -1.387e+08        nan        nan        nan         nan         nan
Cost of a healthy diet                -5.6642      2.898     -1.955      0.051     -11.344       0.016
Income                                -1.2168      0.285     -4.270      0.000      -1.775      -0.658
Inflation                              0.1587      0.067      2.372      0.018       0.028       0.290
Child mortality rate                 -26.6933      1.192    -22.396      0.000     -29.029     -24.357
Unemployment Rate                     -0.2086      0.297     -0.702      0.483      -0.791       0.374
Incomplete tertiary education          0.2633      0.131      2.003      0.045       0.006       0.521
Gini coefficient                      12.2883     15.458      0.795      0.427     -18.011      42.588
Sex ratio                           2.196e+08   4.07e+09      0.054      0.957   -7.77e+09     8.2e+09
GDP                                    0.1197      0.081      1.469      0.142      -0.040       0.279
Median age                           502.6744    576.528      0.872      0.383    -627.380    1632.729
CPI                                   -0.0719      0.024     -3.044      0.002      -0.118      -0.026
BMI_avg                               -0.0414      0.039     -1.073      0.283      -0.117       0.034
Cost of a healthy diet_lag1            0.5269      1.822      0.289      0.772      -3.044       4.098
Cost of a healthy diet_lag2           -0.3998      2.777     -0.144      0.886      -5.843       5.043
Cost of a healthy diet_lag3            4.8015      3.009      1.596      0.111      -1.096      10.699
Income_lag1                            0.1039      0.201      0.518      0.605      -0.289       0.497
Income_lag2                            0.0726      0.244      0.297      0.767      -0.407       0.552
Income_lag3                            1.1014      0.325      3.393      0.001       0.465       1.738
Inflation_lag1                         0.0803      0.042      1.923      0.054      -0.002       0.162
Inflation_lag2                         0.0255      0.042      0.612      0.541      -0.056       0.107
Inflation_lag3                        -0.1654      0.077     -2.155      0.031      -0.316      -0.015
Child mortality rate_lag1              0.0505      1.106      0.046      0.964      -2.116       2.217
Child mortality rate_lag2              3.3981      1.241      2.739      0.006       0.966       5.830
Child mortality rate_lag3             10.0025        nan        nan        nan         nan         nan
Unemployment Rate_lag1                -0.3134      0.225     -1.396      0.163      -0.754       0.127
Unemployment Rate_lag2                 0.5150      0.240      2.142      0.032       0.044       0.986
Unemployment Rate_lag3                -0.1186      0.281     -0.422      0.673      -0.669       0.432
Incomplete tertiary education_lag1    -0.0128      0.125     -0.103      0.918      -0.257       0.231
Incomplete tertiary education_lag2     0.0864      0.159      0.544      0.587      -0.225       0.398
Incomplete tertiary education_lag3    -0.3514      0.200     -1.758      0.079      -0.743       0.040
Gini coefficient_lag1                  4.2493      8.833      0.481      0.630     -13.064      21.563
Gini coefficient_lag2                 -2.9126      8.570     -0.340      0.734     -19.711      13.886
Gini coefficient_lag3                -18.9249      9.612     -1.969      0.049     -37.765      -0.085
Sex ratio_lag1                      4.569e+07   8.87e+09      0.005      0.996   -1.73e+10    1.74e+10
Sex ratio_lag2                      1.123e+08    7.4e+09      0.015      0.988   -1.44e+10    1.46e+10
Sex ratio_lag3                      1.381e+08   1.97e+09      0.070      0.944   -3.73e+09    4.01e+09
GDP_lag1                               0.0178      0.077      0.231      0.817      -0.133       0.169
GDP_lag2                              -0.0076      0.025     -0.310      0.757      -0.056       0.041
GDP_lag3                              -0.0259      0.051     -0.508      0.611      -0.126       0.074
Median age_lag1                     -618.4803   1131.103     -0.547      0.585   -2835.561    1598.600
Median age_lag2                     -418.3976   1146.221     -0.365      0.715   -2665.111    1828.316
Median age_lag3                      503.8654    599.070      0.841      0.400    -670.375    1678.105
CPI_lag1                              -0.0128      0.017     -0.771      0.441      -0.045       0.020
CPI_lag2                               0.0047      0.015      0.317      0.751      -0.024       0.034
CPI_lag3                               0.0772      0.016      4.783      0.000       0.046       0.109
BMI_avg_lag1                          -0.0056      0.026     -0.218      0.828      -0.056       0.045
BMI_avg_lag2                          -0.0167      0.028     -0.585      0.558      -0.072       0.039
BMI_avg_lag3                           0.0151      0.041      0.365      0.715      -0.066       0.096
==============================================================================
Omnibus:                     2958.623   Durbin-Watson:                   0.136
Prob(Omnibus):                  0.000   Jarque-Bera (JB):            10571.972
Skew:                          -0.860   Prob(JB):                         0.00
Kurtosis:                       6.468   Cond. No.                     6.74e+11
==============================================================================

Notes:
[1] Standard Errors are heteroscedasticity and autocorrelation robust (HAC) using 5 lags and without small sample correction
[2] The smallest eigenvalue is 2.9e-16. This might indicate that there are
strong multicollinearity problems or that the design matrix is singular.

📊 Residuals Summary for 'Life expectancy':
count    16928.000000
mean         0.000003
std          3.364682
min        -32.524090
25%         -1.963268
50%          0.267816
75%          2.256538
max         15.502762
dtype: float64

/usr/local/lib/python3.11/dist-packages/statsmodels/base/model.py:1894: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 48, but rank is 4
  warnings.warn('covariance of constraints does not have full '
/usr/local/lib/python3.11/dist-packages/statsmodels/regression/linear_model.py:1884: RuntimeWarning: invalid value encountered in sqrt
  return np.sqrt(np.diag(self.cov_params()))

=== Newey-West Adjusted OLS Results for: Cardiovascular diseases ===
                               OLS Regression Results                              
===================================================================================
Dep. Variable:     Cardiovascular diseases   R-squared:                       0.043
Model:                                 OLS   Adj. R-squared:                  0.040
Method:                      Least Squares   F-statistic:                   0.01330
Date:                     Thu, 24 Jul 2025   Prob (F-statistic):               1.00
Time:                             19:55:10   Log-Likelihood:            -1.0767e+05
No. Observations:                    16928   AIC:                         2.154e+05
Df Residuals:                        16879   BIC:                         2.158e+05
Df Model:                               48                                         
Covariance Type:                       HAC                                         
======================================================================================================
                                         coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------------------------------
const                              -4.323e+09   2.21e+09     -1.955      0.051   -8.66e+09    1.02e+07
Cost of a healthy diet                12.5572     32.502      0.386      0.699     -51.151      76.265
Income                                21.3556     17.251      1.238      0.216     -12.457      55.169
Inflation                              1.1911      2.883      0.413      0.679      -4.460       6.842
Child mortality rate                  -1.6502     55.220     -0.030      0.976    -109.888     106.588
Unemployment Rate                     16.5400      9.638      1.716      0.086      -2.352      35.432
Incomplete tertiary education         -5.2535      7.608     -0.691      0.490     -20.166       9.659
Gini coefficient                     160.8072    595.017      0.270      0.787   -1005.488    1327.102
Sex ratio                           7.411e+09   1.32e+11      0.056      0.955   -2.51e+11    2.66e+11
GDP                                    3.3278      2.869      1.160      0.246      -2.295       8.950
Median age                          1097.7356   1.82e+04      0.060      0.952   -3.45e+04    3.67e+04
CPI                                    0.4940      1.274      0.388      0.698      -2.002       2.990
BMI_avg                               -1.7352      1.782     -0.974      0.330      -5.228       1.757
Cost of a healthy diet_lag1            9.9738        nan        nan        nan         nan         nan
Cost of a healthy diet_lag2           11.7875     68.555      0.172      0.863    -122.587     146.162
Cost of a healthy diet_lag3            1.1933     37.275      0.032      0.974     -71.869      74.256
Income_lag1                            0.4962      8.482      0.058      0.953     -16.130      17.123
Income_lag2                           13.8650     19.042      0.728      0.467     -23.459      51.189
Income_lag3                          -12.2323     13.529     -0.904      0.366     -38.751      14.287
Inflation_lag1                        -0.4568      1.486     -0.307      0.759      -3.370       2.457
Inflation_lag2                        -0.6003      1.574     -0.381      0.703      -3.686       2.485
Inflation_lag3                        -1.7078      3.475     -0.491      0.623      -8.519       5.103
Child mortality rate_lag1              1.6709        nan        nan        nan         nan         nan
Child mortality rate_lag2              2.8105        nan        nan        nan         nan         nan
Child mortality rate_lag3              8.4775     17.638      0.481      0.631     -26.096      43.051
Unemployment Rate_lag1                -0.7751      2.350     -0.330      0.742      -5.382       3.832
Unemployment Rate_lag2                 0.5853      0.323      1.811      0.070      -0.048       1.219
Unemployment Rate_lag3                -5.4142      5.114     -1.059      0.290     -15.438       4.609
Incomplete tertiary education_lag1    -0.0505      4.402     -0.011      0.991      -8.679       8.578
Incomplete tertiary education_lag2     0.4994      1.740      0.287      0.774      -2.911       3.909
Incomplete tertiary education_lag3    -2.5220      7.285     -0.346      0.729     -16.801      11.757
Gini coefficient_lag1                 50.2384    134.988      0.372      0.710    -214.351     314.828
Gini coefficient_lag2                197.9054    214.176      0.924      0.355    -221.902     617.712
Gini coefficient_lag3               -761.0729    290.835     -2.617      0.009   -1331.140    -191.006
Sex ratio_lag1                      1.369e+09        nan        nan        nan         nan         nan
Sex ratio_lag2                      1.918e+09        nan        nan        nan         nan         nan
Sex ratio_lag3                      5.383e+09        nan        nan        nan         nan         nan
GDP_lag1                               0.2412        nan        nan        nan         nan         nan
GDP_lag2                              -0.0958        nan        nan        nan         nan         nan
GDP_lag3                               1.1461      2.557      0.448      0.654      -3.867       6.159
Median age_lag1                    -1056.8683   3.13e+04     -0.034      0.973   -6.24e+04    6.03e+04
Median age_lag2                     5836.9081        nan        nan        nan         nan         nan
Median age_lag3                    -3728.3101        nan        nan        nan         nan         nan
CPI_lag1                               0.0970      0.685      0.142      0.887      -1.247       1.441
CPI_lag2                               0.1355      0.706      0.192      0.848      -1.248       1.519
CPI_lag3                               1.4261      1.293      1.103      0.270      -1.108       3.961
BMI_avg_lag1                          -0.1263      0.903     -0.140      0.889      -1.896       1.643
BMI_avg_lag2                          -0.0982      0.921     -0.107      0.915      -1.904       1.708
BMI_avg_lag3                          -3.5173      1.732     -2.031      0.042      -6.912      -0.123
==============================================================================
Omnibus:                    26274.800   Durbin-Watson:                   0.029
Prob(Omnibus):                  0.000   Jarque-Bera (JB):          9869872.713
Skew:                          10.133   Prob(JB):                         0.00
Kurtosis:                     119.544   Cond. No.                     6.74e+11
==============================================================================

Notes:
[1] Standard Errors are heteroscedasticity and autocorrelation robust (HAC) using 5 lags and without small sample correction
[2] The smallest eigenvalue is 2.9e-16. This might indicate that there are
strong multicollinearity problems or that the design matrix is singular.

📊 Residuals Summary for 'Cardiovascular diseases':
count    16928.000000
mean         0.000031
std        140.026594
min       -114.217116
25%        -36.951825
50%        -17.545906
75%          2.043523
max       1848.846904
dtype: float64

/usr/local/lib/python3.11/dist-packages/statsmodels/base/model.py:1894: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 48, but rank is 4
  warnings.warn('covariance of constraints does not have full '
/usr/local/lib/python3.11/dist-packages/statsmodels/regression/linear_model.py:1884: RuntimeWarning: invalid value encountered in sqrt
  return np.sqrt(np.diag(self.cov_params()))

=== Newey-West Adjusted OLS Results for: Diabetes ===
                            OLS Regression Results                            
==============================================================================
Dep. Variable:               Diabetes   R-squared:                       0.539
Model:                            OLS   Adj. R-squared:                  0.537
Method:                 Least Squares   F-statistic:                     2.167
Date:                Thu, 24 Jul 2025   Prob (F-statistic):             0.0700
Time:                        19:55:11   Log-Likelihood:                -43381.
No. Observations:               16928   AIC:                         8.686e+04
Df Residuals:                   16879   BIC:                         8.724e+04
Df Model:                          48                                         
Covariance Type:                  HAC                                         
======================================================================================================
                                         coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------------------------------
const                              -1.294e+08        nan        nan        nan         nan         nan
Cost of a healthy diet                 6.7514      1.664      4.058      0.000       3.490      10.013
Income                                -1.0163      0.277     -3.675      0.000      -1.558      -0.474
Inflation                             -0.1576      0.059     -2.650      0.008      -0.274      -0.041
Child mortality rate                  -0.1721      0.736     -0.234      0.815      -1.616       1.271
Unemployment Rate                      0.0279      0.183      0.152      0.879      -0.331       0.387
Incomplete tertiary education         -0.2060      0.048     -4.335      0.000      -0.299      -0.113
Gini coefficient                     -11.5868        nan        nan        nan         nan         nan
Sex ratio                           1.678e+08   3.41e+09      0.049      0.961   -6.52e+09    6.85e+09
GDP                                   -0.1475      0.030     -4.868      0.000      -0.207      -0.088
Median age                           503.8324    343.204      1.468      0.142    -168.883    1176.548
CPI                                    0.0779      0.022      3.592      0.000       0.035       0.120
BMI_avg                               -0.0456      0.057     -0.799      0.424      -0.157       0.066
Cost of a healthy diet_lag1            0.0382      0.929      0.041      0.967      -1.783       1.860
Cost of a healthy diet_lag2            0.8687      1.825      0.476      0.634      -2.708       4.446
Cost of a healthy diet_lag3            0.7153      2.080      0.344      0.731      -3.362       4.792
Income_lag1                           -0.2537      0.185     -1.369      0.171      -0.617       0.110
Income_lag2                           -0.0917      0.262     -0.350      0.726      -0.606       0.422
Income_lag3                           -0.5161      0.281     -1.835      0.067      -1.067       0.035
Inflation_lag1                        -0.0909      0.038     -2.403      0.016      -0.165      -0.017
Inflation_lag2                        -0.0971      0.044     -2.215      0.027      -0.183      -0.011
Inflation_lag3                        -0.1121      0.049     -2.288      0.022      -0.208      -0.016
Child mortality rate_lag1             -0.3867      0.523     -0.739      0.460      -1.412       0.639
Child mortality rate_lag2              0.0770      0.278      0.277      0.782      -0.469       0.623
Child mortality rate_lag3              0.6715      0.251      2.675      0.007       0.179       1.164
Unemployment Rate_lag1                -0.0285      0.107     -0.268      0.789      -0.237       0.180
Unemployment Rate_lag2                -0.0775      0.092     -0.845      0.398      -0.257       0.102
Unemployment Rate_lag3                -0.1296      0.188     -0.689      0.491      -0.498       0.239
Incomplete tertiary education_lag1     0.0421      0.092      0.457      0.648      -0.138       0.223
Incomplete tertiary education_lag2    -0.0203      0.120     -0.169      0.866      -0.256       0.216
Incomplete tertiary education_lag3    -0.1239      0.153     -0.811      0.417      -0.423       0.176
Gini coefficient_lag1                 -7.6403      7.914     -0.965      0.334     -23.153       7.873
Gini coefficient_lag2                 -1.8097      5.717     -0.317      0.752     -13.015       9.395
Gini coefficient_lag3                  1.5183      8.841      0.172      0.864     -15.810      18.847
Sex ratio_lag1                      1.984e+07   5.37e+09      0.004      0.997   -1.05e+10    1.05e+10
Sex ratio_lag2                      4.239e+07        nan        nan        nan         nan         nan
Sex ratio_lag3                      2.511e+08        nan        nan        nan         nan         nan
GDP_lag1                              -0.0006      0.052     -0.012      0.990      -0.103       0.102
GDP_lag2                              -0.0003        nan        nan        nan         nan         nan
GDP_lag3                              -0.0483      0.041     -1.165      0.244      -0.130       0.033
Median age_lag1                     -718.9570    695.114     -1.034      0.301   -2081.454     643.539
Median age_lag2                     1089.5169    611.331      1.782      0.075    -108.757    2287.790
Median age_lag3                     -996.5964    382.339     -2.607      0.009   -1746.020    -247.173
CPI_lag1                              -0.0030      0.013     -0.234      0.815      -0.028       0.022
CPI_lag2                              -0.0011      0.014     -0.080      0.937      -0.028       0.026
CPI_lag3                               0.0204      0.023      0.882      0.378      -0.025       0.066
BMI_avg_lag1                          -0.0006      0.031     -0.021      0.984      -0.060       0.059
BMI_avg_lag2                          -0.0014      0.034     -0.042      0.967      -0.068       0.065
BMI_avg_lag3                           0.9702      0.056     17.417      0.000       0.861       1.079
==============================================================================
Omnibus:                     4120.296   Durbin-Watson:                   0.108
Prob(Omnibus):                  0.000   Jarque-Bera (JB):            12682.365
Skew:                           1.250   Prob(JB):                         0.00
Kurtosis:                       6.425   Cond. No.                     6.74e+11
==============================================================================

Notes:
[1] Standard Errors are heteroscedasticity and autocorrelation robust (HAC) using 5 lags and without small sample correction
[2] The smallest eigenvalue is 2.9e-16. This might indicate that there are
strong multicollinearity problems or that the design matrix is singular.

📊 Residuals Summary for 'Diabetes':
count    16928.000000
mean         0.000001
std          3.138661
min         -9.906907
25%         -2.044869
50%         -0.612431
75%          1.408433
max         24.396272
dtype: float64

/usr/local/lib/python3.11/dist-packages/statsmodels/base/model.py:1894: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 48, but rank is 4
  warnings.warn('covariance of constraints does not have full '
/usr/local/lib/python3.11/dist-packages/statsmodels/regression/linear_model.py:1884: RuntimeWarning: invalid value encountered in sqrt
  return np.sqrt(np.diag(self.cov_params()))

# Stable Predictors Bar Charts

import matplotlib.pyplot as plt
import seaborn as sns

# Convert p-values to float for plotting
stable_summary_df['p-value'] = stable_summary_df['p-value < 0.05'].astype(float)

# Set plot style
sns.set(style="whitegrid")

# Get list of unique targets
targets = stable_summary_df['Target'].unique()

# Create one bar chart per target
for target in targets:
    plt.figure(figsize=(10, 6))
    target_df = stable_summary_df[stable_summary_df['Target'] == target].copy()

    # Sort by p-value (lowest = most significant)
    target_df = target_df.sort_values('p-value', ascending=True)

    # Barplot of -log10(p-value) for visibility
    sns.barplot(
        data=target_df,
        x=-np.log10(target_df['p-value']),  # higher bar = more significant
        y='Stable Predictor',
        palette='viridis'
    )

    plt.title(f"Stable Predictors for {target} (p < 0.05)", fontsize=14)
    plt.xlabel('-log10(p-value)')
    plt.ylabel('Predictor')
    plt.tight_layout()
    plt.show()

/tmp/ipython-input-21-1853306393.py:24: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.

  sns.barplot(

/tmp/ipython-input-21-1853306393.py:24: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.

  sns.barplot(

/tmp/ipython-input-21-1853306393.py:24: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.

  sns.barplot(

# Forecast Comparison of ARIMA, Prophet, and Random Forest Across Three Targets – Life Expectancy, Diabetes and Cardiovascular Disease between 1950–2074

import matplotlib.pyplot as plt
import numpy as np

def plot_target_forecast(df_model_all, df_eval_ready, country, target):
    # Years
    full_years = list(range(1950, 2075))
    eval_years = [2021, 2022, 2023]
    forecast_start, forecast_end = 2024, 2074

    # === Actual values for 1950–2023
    df_actual = df_eval_ready[
        (df_eval_ready['Country'] == country) &
        (df_eval_ready['Year'].between(1950, 2023))
    ].sort_values('Year')
    actual_years = df_actual['Year'].values
    actual_vals = df_actual[target].values

    # === Predictions from df_model_comparison for 2021–2074
    df_pred = df_model_all[
        (df_model_all['Country'] == country) &
        (df_model_all['Target'] == target) &
        (df_model_all['Year'].between(2021, 2074))
    ].sort_values('Year')
    pred_years = df_pred['Year'].values
    rf_vals = df_pred['RF_Forecast'].values
    arima_vals = df_pred['ARIMA_Forecast'].values
    prophet_vals = df_pred['Prophet_Forecast'].values

    # Split prediction into eval + forecast ranges
    rf_eval, rf_forecast = [], []
    arima_eval, arima_forecast = [], []
    prophet_eval, prophet_forecast = [], []

    for yr, rf, ar, pr in zip(pred_years, rf_vals, arima_vals, prophet_vals):
        if yr in eval_years:
            rf_eval.append((yr, rf))
            arima_eval.append((yr, ar))
            prophet_eval.append((yr, pr))
        else:
            rf_forecast.append((yr, rf))
            arima_forecast.append((yr, ar))
            prophet_forecast.append((yr, pr))

    # Begin plot
    plt.figure(figsize=(14, 6))

    # Shaded forecast area
    plt.axvspan(forecast_start, forecast_end, color='gray', alpha=0.12, label="Forecast Horizon")

    # Actual line
    plt.plot(actual_years, actual_vals, label="Actual", color='orange', linewidth=2)

    # Prediction lines (2021–2023)
    if rf_eval: plt.plot(*zip(*rf_eval), label="RF Eval", color='dodgerblue', linestyle='dashed', linewidth=2)
    if arima_eval: plt.plot(*zip(*arima_eval), label="ARIMA Eval", color='forestgreen', linestyle='dashed', linewidth=2)
    if prophet_eval: plt.plot(*zip(*prophet_eval), label="Prophet Eval", color='darkorchid', linestyle='dashed', linewidth=2)

    # Forecast lines (2024–2074)
    if rf_forecast: plt.plot(*zip(*rf_forecast), label="RF Forecast", color='dodgerblue', linewidth=2)
    if arima_forecast: plt.plot(*zip(*arima_forecast), label="ARIMA Forecast", color='forestgreen', linewidth=2)
    if prophet_forecast: plt.plot(*zip(*prophet_forecast), label="Prophet Forecast", color='darkorchid', linewidth=2)

    # Final plot touches
    plt.title(f"{target} — Actual, Evaluation & Forecast Comparison ({country})", fontsize=16)
    plt.xlabel("Year")
    plt.ylabel("Value")
    plt.grid(True)
    plt.legend()
    plt.xlim(1950, 2074)
    plt.tight_layout()
    plt.show()

selected_countries = [
    'United States', 'Germany', 'Japan', 'Brazil', 'India',
    'Indonesia', 'Nigeria', 'Kenya', 'Mexico', 'Bangladesh'
]
selected_targets = ["Life expectancy", "Diabetes", "Cardiovascular diseases"]

for country in selected_countries:
    for target in selected_targets:
        plot_target_forecast(df_model_comparison, df_forecast_ready, country, target)

# Plot comparison for 4 countries

import matplotlib.pyplot as plt
import seaborn as sns

# Countries and years to plot
countries_to_plot = ['United States', 'Mexico', 'India', 'Japan']
years_to_plot = [2021, 2022, 2023]

# combine all result into one dataframe
val_results = pd.concat([
    pd.concat(arima_val_all, ignore_index=True),
    pd.concat(prophet_val_all, ignore_index=True),
    pd.concat(rf_val_all, ignore_index=True)
], ignore_index=True)

val_results['Model'] = val_results['Model'].replace({'RandomForest': 'Random Forest'})

# Filter validation results for these countries and years
plot_df = val_results[
    (val_results['Country'].isin(countries_to_plot)) &
    (val_results['Year'].isin(years_to_plot))
].copy()

# Example for one target variable, say target = 'Cardiovascular diseases'
target_of_interest = 'Cardiovascular diseases'
plot_df = plot_df[plot_df['Target'] == target_of_interest]

# Set seaborn style
sns.set(style="whitegrid")

# Create a separate plot for each country with actual vs predicted lines for each model
fig, axs = plt.subplots(2, 2, figsize=(16, 10), sharey=True)
axs = axs.flatten()

for i, country in enumerate(countries_to_plot):
    ax = axs[i]
    country_data = plot_df[plot_df['Country'] == country]

    # Plot Actual values
    actual_data = country_data[['Year', 'Actual']].drop_duplicates()
    ax.plot(actual_data['Year'], actual_data['Actual'], label='Actual', color='black', marker='o')

    # Plot Forecasts from each model
    for model in country_data['Model'].unique():
        model_data = country_data[country_data['Model'] == model]
        ax.plot(model_data['Year'], model_data['Forecast'], label=f'Forecast ({model})', marker='x')

    ax.set_title(f'{country} - Actual vs Predicted ({target_of_interest})')
    ax.set_xlabel('Year')
    ax.set_ylabel('Value')
    ax.legend()
    ax.grid(True)

plt.tight_layout()
plt.show()

# Rolling Forecast - Walk Forward Validation

from statsmodels.tsa.arima.model import ARIMA
from prophet import Prophet
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

# === Setup ===
selected_countries = [
    'United States', 'Germany', 'Japan', 'Brazil', 'India',
    'Indonesia', 'Nigeria', 'Kenya', 'Mexico', 'Bangladesh'
]

target_columns = ['Life expectancy', 'Cardiovascular diseases', 'Diabetes']

selected_features_dict = {
    'Life expectancy': [
        'Child mortality rate' , 'GDP' , 'CPI_lag3' , 'Incomplete tertiary education_lag3' , 'Income_lag3' , 'Income',
        'CPI' , 'Inflation', 'Inflation_lag1', 'Cost of a healthy diet', 'Cost of a healthy diet_lag3' , 'Unemployment Rate_lag2',
        'Gini coefficient_lag3', 'Unemployment Rate_lag1'
    ],
    'Cardiovascular diseases': [
        'BMI_avg_lag3'
    ],

    'Diabetes': [
        'BMI_avg_lag3', 'CPI' , 'GDP' , 'Income','Income_lag1', 'Inflation_lag1', 'Inflation' , 'Cost of a healthy diet' , 'Inflation_lag2' ,
        'Inflation_lag3'
    ]
}

start_train = 1950
end_train = 2020
real_eval_period = [2021, 2022, 2023]

# Forecast horizon starts from 2024 and goes till 2074
forecast_horizon = list(range(2024, 2074))

# === Create future rows for years 2024 to 2073
future_rows = []
for country in df_combined_with_country['Country'].unique():
    for year in forecast_horizon:
        future_rows.append({'Country': country, 'Year': year})

df_future = pd.DataFrame(future_rows)
df_forecast_ready = pd.concat([df_combined_with_country, df_future], ignore_index=True)
df_forecast_ready['Year'] = df_forecast_ready['Year'].astype(int)

# === Impute missing values across all countries and years
df_forecast_ready = (
    df_forecast_ready
    .sort_values(['Country', 'Year'])
    .groupby('Country', group_keys=False)
    .apply(lambda x: x.ffill().bfill().infer_objects(copy=False))
    .reset_index(drop=True)
)

# === Initialize summary table
predictions_summary = []

# === Forecast Loop ===
for country in selected_countries:
    df_country = df_forecast_ready[df_forecast_ready['Country'] == country].sort_values('Year')

    for target in target_columns:
        print(f"\n {country} —  {target}")
        if target not in df_country.columns:
            print(" Target missing")
            continue

        features = selected_features_dict.get(target, [])
        available_features = [f for f in features if f in df_country.columns]
        if not available_features:
            print(" No usable features found")
            continue

        df_train = df_country[df_country['Year'].between(start_train, end_train)]
        df_eval_real = df_country[df_country['Year'].isin(real_eval_period)]

        # === ARIMA ===
        arima_rmse = None
        try:
            df_train_arima = df_train[[target]].copy()
            df_train_arima.index = pd.date_range(start=f'{start_train}', periods=len(df_train_arima), freq='YE')
            model_arima = ARIMA(df_train_arima, order=(1, 1, 1)).fit()

            # Real evaluation
            pred_real = model_arima.predict(start=len(df_train_arima), end=len(df_train_arima)+len(df_eval_real)-1)
            actual_real = df_eval_real[target].values
            arima_rmse = np.sqrt(mean_squared_error(actual_real, pred_real))

            # Forecast for 2024-2073
            arima_forecast = model_arima.predict(start=len(df_train_arima), end=len(df_train_arima) + len(forecast_horizon) - 1)
            print(f"📉 ARIMA RMSE: {arima_rmse:.2f}")
        except Exception as e:
            print(f" ARIMA error: {e}")

        # === Prophet ===
        prophet_rmse = None
        try:
            prophet_df = df_train[['Year', target]].rename(columns={'Year': 'ds', target: 'y'})
            prophet_df['ds'] = pd.to_datetime(prophet_df['ds'], format='%Y')
            model_prophet = Prophet()
            model_prophet.fit(prophet_df)

            future_years = real_eval_period + forecast_horizon
            future_dates = pd.DataFrame({'ds': pd.to_datetime(future_years, format='%Y')})
            forecast_prophet = model_prophet.predict(future_dates)

            # Real evaluation
            pred_real = forecast_prophet[forecast_prophet['ds'].dt.year.isin(real_eval_period)]['yhat'].values
            actual_real = df_eval_real[target].values
            prophet_rmse = np.sqrt(mean_squared_error(actual_real, pred_real))

            # Forecast for 2024-2073
            prophet_forecast = forecast_prophet[forecast_prophet['ds'].dt.year.isin(forecast_horizon)]
            print(f" Prophet RMSE: {prophet_rmse:.2f}")
        except Exception as e:
            print(f" Prophet error: {e}")

      #### Random Forest ####
        #from google.colab import data_table
        #data_table.DataTable(df_forecast)

        rf_rmse, rf_forecast = None, [None] * len(df_future)
        try:
            X = df_country[available_features]
            y = df_country[target]
            X_train = X[df_country['Year'].between(start_train, end_train)]
            y_train = y[df_country['Year'].between(start_train, end_train)]
            X_eval = X[df_country['Year'].isin(real_eval_period)]
            y_eval = y[df_country['Year'].isin(real_eval_period)]
            model = RandomForestRegressor(n_estimators=100, random_state=42)
            model.fit(X_train, y_train)
            pred_eval = model.predict(X_eval)
            rf_rmse = np.sqrt(mean_squared_error(y_eval, pred_eval))
            X_forecast = X[df_country['Year'].isin(forecast_horizon)]
            if not X_forecast.isnull().any(axis=1).any():
                rf_forecast = model.predict(X_forecast).tolist()
        except:
            pass


        # === Append to summary ===
        predictions_summary.append({
            "Country": country,
            "Target": target,
            "ARIMA_RMSE": round(arima_rmse, 4) if arima_rmse is not None else None,
            "Prophet_RMSE": round(prophet_rmse, 4) if prophet_rmse is not None else None,
            "RF_RMSE": round(rf_rmse, 4) if rf_rmse is not None else None
        })

# === Final Summary Table ===
df_forecast_validation_summary = pd.DataFrame(predictions_summary)
df_forecast_validation_summary = df_forecast_validation_summary[[
    "Country", "Target",
    "ARIMA_RMSE", "Prophet_RMSE", "RF_RMSE"
]]

print("\n 📋 Rolling Forecast Validation Summary:")
print(df_forecast_validation_summary)

# Export summary
df_forecast_validation_summary.to_csv("forecast_summary.csv", index=False)

# Download to your computer
from google.colab import files
files.download("forecast_summary.csv")

/tmp/ipython-input-37-3481924030.py:57: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
  .apply(lambda x: x.ffill().bfill().infer_objects(copy=False))
/tmp/ipython-input-37-3481924030.py:57: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
  .apply(lambda x: x.ffill().bfill().infer_objects(copy=False))
/usr/local/lib/python3.11/dist-packages/statsmodels/tsa/statespace/sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'

 United States —  Life expectancy
📉 ARIMA RMSE: 2.00

INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/hv2ktus1.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/oe_r3aoy.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=47361', 'data', 'file=/tmp/tmprjkocm4m/hv2ktus1.json', 'init=/tmp/tmprjkocm4m/oe_r3aoy.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_model3c9z48hr/prophet_model-20250723141100.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
14:11:00 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
14:11:02 - cmdstanpy - INFO - Chain [1] done processing
INFO:cmdstanpy:Chain [1] done processing

 Prophet RMSE: 1.56

 United States —  Cardiovascular diseases

INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/sa5_nkw_.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/pn5w6eso.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=25332', 'data', 'file=/tmp/tmprjkocm4m/sa5_nkw_.json', 'init=/tmp/tmprjkocm4m/pn5w6eso.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelyycjtjmt/prophet_model-20250723141104.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
14:11:04 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing

📉 ARIMA RMSE: 1.19

14:11:04 - cmdstanpy - INFO - Chain [1] done processing
INFO:cmdstanpy:Chain [1] done processing

 Prophet RMSE: 11.97

 United States —  Diabetes

INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/sghhl48c.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/ahdz28xn.json
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📉 ARIMA RMSE: 0.01

# Step 19 Final Model Training & Forecasting - ok
import pandas as pd
import numpy as np
from sklearn.metrics import mean_squared_error
from sklearn.ensemble import RandomForestRegressor
from statsmodels.tsa.arima.model import ARIMA
from prophet import Prophet
import warnings
import logging

warnings.filterwarnings("ignore")
logging.getLogger('statsmodels').setLevel(logging.ERROR)

# === Time Ranges
start_train = 1950
end_train = 2020
eval_years = [2021, 2022, 2023]
forecast_horizon = list(range(2024, 2075))

# === Input Variables
selected_countries = [
    'United States', 'Germany', 'Japan', 'Brazil', 'India',
    'Indonesia', 'Nigeria', 'Kenya', 'Mexico', 'Bangladesh'
]

target_columns = ['Life expectancy', 'Cardiovascular diseases', 'Diabetes']

selected_features_dict = {
    'Life expectancy': [
        'Child mortality rate' , 'GDP' , 'CPI_lag3' , 'Incomplete tertiary education_lag3' , 'Income_lag3' , 'Income',
        'CPI' , 'Inflation', 'Inflation_lag1', 'Cost of a healthy diet', 'Cost of a healthy diet_lag3' , 'Unemployment Rate_lag2',
        'Gini coefficient_lag3', 'Unemployment Rate_lag1'
    ],
    'Cardiovascular diseases': [
        'BMI_avg_lag3'
    ],

    'Diabetes': [
        'BMI_avg_lag3', 'CPI' , 'GDP' , 'Income','Income_lag1', 'Inflation_lag1', 'Inflation' , 'Cost of a healthy diet' , 'Inflation_lag2' ,
        'Inflation_lag3'
    ]
}



# === Ready Dataset (already loaded)
# df_forecast_ready = your real dataset

# === Forecasting and Evaluation
forecast_summary = []

for country in selected_countries:
    df_country = df_forecast_ready[df_forecast_ready['Country'] == country].sort_values('Year')

    for target in target_columns:
        if target not in df_country.columns:
            continue

        features = selected_features_dict.get(target, [])
        available_features = [f for f in features if f in df_country.columns]
        if not available_features:
            continue

        df_train = df_country[df_country['Year'].between(start_train, end_train)]
        df_eval = df_country[df_country['Year'].isin(eval_years)]
        df_forecast = df_country[df_country['Year'].isin(forecast_horizon)]
        actual_eval = df_eval[target].values

        #### ARIMA ####
        arima_rmse, arima_forecast = None, [None] * len(df_forecast)
        try:
            train_series = df_train[[target]].copy()
            train_series.index = pd.date_range(start='1950', periods=len(train_series), freq='YE')
            model = ARIMA(train_series, order=(1, 1, 1)).fit()
            pred_eval = model.predict(start=len(train_series), end=len(train_series) + len(df_eval) - 1)
            arima_rmse = np.sqrt(mean_squared_error(actual_eval, pred_eval))
            arima_forecast = model.predict(start=len(train_series) + len(df_eval),
                                           end=len(train_series) + len(df_eval) + len(df_forecast) - 1).tolist()
        except:
            pass

        #### Prophet ####
        prophet_rmse, prophet_forecast = None, [None] * len(df_forecast)
        try:
            prophet_df = df_train[['Year', target]].rename(columns={'Year': 'ds', target: 'y'})
            prophet_df['ds'] = pd.to_datetime(prophet_df['ds'], format='%Y')
            model = Prophet()
            model.fit(prophet_df)
            eval_dates = pd.DataFrame({'ds': pd.to_datetime(eval_years, format='%Y')})
            forecast_eval = model.predict(eval_dates)
            prophet_rmse = np.sqrt(mean_squared_error(actual_eval, forecast_eval['yhat'].values))
            forecast_years = pd.DataFrame({'ds': pd.to_datetime(df_forecast['Year'], format='%Y')})
            prophet_forecast = model.predict(forecast_years)['yhat'].tolist()
        except:
            pass

        #### Random Forest ####
        rf_rmse, rf_forecast = None, [None] * len(df_forecast)
        try:
            X = df_country[available_features]
            y = df_country[target]
            X_train = X[df_country['Year'].between(start_train, end_train)]
            y_train = y[df_country['Year'].between(start_train, end_train)]
            X_eval = X[df_country['Year'].isin(eval_years)]
            y_eval = y[df_country['Year'].isin(eval_years)]
            model = RandomForestRegressor(n_estimators=100, random_state=42)
            model.fit(X_train, y_train)
            pred_eval = model.predict(X_eval)
            rf_rmse = np.sqrt(mean_squared_error(y_eval, pred_eval))
            X_forecast = X[df_country['Year'].isin(forecast_horizon)]
            if not X_forecast.isnull().any(axis=1).any():
                rf_forecast = model.predict(X_forecast).tolist()
        except:
            pass

        for i, year in enumerate(df_forecast['Year']):
            forecast_summary.append({
                "Country": country,
                "Target": target,
                "Year": year,
                "ARIMA_RMSE": arima_rmse,
                "ARIMA_Forecast": arima_forecast[i],
                "Prophet_RMSE": prophet_rmse,
                "Prophet_Forecast": prophet_forecast[i],
                "RF_RMSE": rf_rmse,
                "RF_Forecast": rf_forecast[i]
            })

# === Combine All Results
df_model_comparison = pd.DataFrame(forecast_summary)

# === Summary Table: Best Model by RMSE
summary_table = df_model_comparison.groupby(['Country', 'Target'])[['ARIMA_RMSE', 'Prophet_RMSE', 'RF_RMSE']].first().reset_index()

def best_model_picker(row):
    scores = {
        'ARIMA': row['ARIMA_RMSE'],
        'Prophet': row['Prophet_RMSE'],
        'RF': row['RF_RMSE']
    }
    return min(scores, key=lambda k: scores[k] if pd.notnull(scores[k]) else np.inf)

summary_table['🎯 Best_Model'] = summary_table.apply(best_model_picker, axis=1)

# === Display Results
print("\n📊 Summary of Best Models per Country and Target:\n")
print(summary_table[['Country', 'Target', 'ARIMA_RMSE', 'Prophet_RMSE', 'RF_RMSE', '🎯 Best_Model']].to_string(index=False))

# === Optional Preview of Forecasts
sample_years = [2025, 2030, 2040, 2050, 2060, 2074]
df_sample = df_model_comparison[df_model_comparison['Year'].isin(sample_years)]
df_sample = df_sample.sort_values(['Country', 'Target', 'Year'])
print("\n📋 Forecasts for Selected Years:\n")
print(df_sample.head(30).to_string(index=False))

# Export summary
df_sample.to_csv("df_sample.csv", index=False)

# Download to your computer
from google.colab import files
files.download("df_sample.csv")

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📊 Summary of Best Models per Country and Target:

      Country                  Target  ARIMA_RMSE  Prophet_RMSE   RF_RMSE 🎯 Best_Model
   Bangladesh Cardiovascular diseases    1.175582      6.991238  4.924493        ARIMA
   Bangladesh                Diabetes    0.000036      2.987844  0.101733        ARIMA
   Bangladesh         Life expectancy    2.312728      1.676697  2.298684      Prophet
       Brazil Cardiovascular diseases    1.819507      6.547227  3.512954        ARIMA
       Brazil                Diabetes    0.000000      0.186005  0.045713        ARIMA
       Brazil         Life expectancy    3.009573      2.189554  1.286215           RF
      Germany Cardiovascular diseases    0.433925      2.125500  0.950348        ARIMA
      Germany                Diabetes    0.000000      2.758175  0.000000        ARIMA
      Germany         Life expectancy    0.474573      0.612408  0.336656           RF
        India Cardiovascular diseases   19.662985     37.420988 47.551155        ARIMA
        India                Diabetes    0.019744      0.830592  0.001732           RF
        India         Life expectancy    1.973657      2.475751  2.190597        ARIMA
    Indonesia Cardiovascular diseases    8.486563      7.998086  0.097082           RF
    Indonesia                Diabetes    0.000000      0.712114  0.003464        ARIMA
    Indonesia         Life expectancy    1.887179      1.692886  1.644150           RF
        Japan Cardiovascular diseases    1.547668      7.688441  4.237571        ARIMA
        Japan                Diabetes    0.000000      1.841061  0.016166        ARIMA
        Japan         Life expectancy    0.638746      0.576474  0.319972           RF
        Kenya Cardiovascular diseases    0.121752      0.933468  0.799274        ARIMA
        Kenya                Diabetes    0.000379      3.479734  0.005196        ARIMA
        Kenya         Life expectancy    3.235337      1.670562  1.293366           RF
       Mexico Cardiovascular diseases    0.578806      0.843693  6.276441        ARIMA
       Mexico                Diabetes    0.000000      0.799705  0.412910        ARIMA
       Mexico         Life expectancy    6.224500      2.428620  2.490150      Prophet
      Nigeria Cardiovascular diseases    0.716350      4.498448  3.617701        ARIMA
      Nigeria                Diabetes    0.000000      0.140798  0.002708        ARIMA
      Nigeria         Life expectancy    0.700330      0.369290  1.244393      Prophet
United States Cardiovascular diseases    1.190369     11.974926 10.091925        ARIMA
United States                Diabetes    0.007983      0.489566  0.004000           RF
United States         Life expectancy    1.996910      1.561422  1.217660           RF

📋 Forecasts for Selected Years:

   Country                  Target  Year  ARIMA_RMSE  ARIMA_Forecast  Prophet_RMSE  Prophet_Forecast  RF_RMSE  RF_Forecast
Bangladesh Cardiovascular diseases  2025    1.175582       30.440474      6.991238         22.343115 4.924493    23.340201
Bangladesh Cardiovascular diseases  2030    1.175582       31.940794      6.991238         24.463361 4.924493    23.340201
Bangladesh Cardiovascular diseases  2040    1.175582       34.260180      6.991238         28.785528 4.924493    23.340201
Bangladesh Cardiovascular diseases  2050    1.175582       35.897214      6.991238         32.425885 4.924493    23.340201
Bangladesh Cardiovascular diseases  2060    1.175582       37.052640      6.991238         36.748052 4.924493    23.340201
Bangladesh                Diabetes  2025    0.000036        9.800033      2.987844          6.593908 0.101733     9.643000
Bangladesh                Diabetes  2030    0.000036        9.800026      2.987844          6.364059 0.101733     9.643000
Bangladesh                Diabetes  2040    0.000036        9.800027      2.987844          5.788692 0.101733     9.643000
Bangladesh                Diabetes  2050    0.000036        9.800027      2.987844          5.211562 0.101733     9.643000
Bangladesh                Diabetes  2060    0.000036        9.800027      2.987844          4.636195 0.101733     9.643000
Bangladesh         Life expectancy  2025    2.312728       71.671340      1.676697         76.010260 2.298684    71.741197
Bangladesh         Life expectancy  2030    2.312728       71.671358      1.676697         77.911348 2.298684    71.741197
Bangladesh         Life expectancy  2040    2.312728       71.671358      1.676697         83.766696 2.298684    71.741197
Bangladesh         Life expectancy  2050    2.312728       71.671358      1.676697         88.736171 2.298684    71.741197
Bangladesh         Life expectancy  2060    2.312728       71.671358      1.676697         94.591519 2.298684    71.741197
    Brazil Cardiovascular diseases  2025    1.819507       37.512576      6.547227         34.048560 3.512954    35.433321
    Brazil Cardiovascular diseases  2030    1.819507       38.026433      6.547227         37.240321 3.512954    35.433321
    Brazil Cardiovascular diseases  2040    1.819507       38.724635      6.547227         43.801768 3.512954    35.433321
    Brazil Cardiovascular diseases  2050    1.819507       39.136841      6.547227         49.309747 3.512954    35.433321
    Brazil Cardiovascular diseases  2060    1.819507       39.380199      6.547227         55.871194 3.512954    35.433321
    Brazil                Diabetes  2025    0.000000        8.300000      0.186005          8.233562 0.045713     8.348000
    Brazil                Diabetes  2030    0.000000        8.300000      0.186005          8.506609 0.045713     8.348000
    Brazil                Diabetes  2040    0.000000        8.300000      0.186005          8.908965 0.045713     8.348000
    Brazil                Diabetes  2050    0.000000        8.300000      0.186005          9.428119 0.045713     8.348000
    Brazil                Diabetes  2060    0.000000        8.300000      0.186005          9.830475 0.045713     8.348000
    Brazil         Life expectancy  2025    3.009573       69.422619      2.189554         77.316414 1.286215    73.905454
    Brazil         Life expectancy  2030    3.009573       65.412065      2.189554         78.606403 1.286215    73.905454
    Brazil         Life expectancy  2040    3.009573       59.751236      2.189554         81.065716 1.286215    73.905454
    Brazil         Life expectancy  2050    3.009573       56.227383      2.189554         83.708557 1.286215    73.905454
    Brazil         Life expectancy  2060    3.009573       54.033793      2.189554         86.167871 1.286215    73.905454

# Summary of Best Models per Country and Target
import pandas as pd
import numpy as np
from sklearn.metrics import mean_squared_error
from sklearn.ensemble import RandomForestRegressor
from statsmodels.tsa.arima.model import ARIMA
from prophet import Prophet
import warnings
import logging

warnings.filterwarnings("ignore")
logging.getLogger('statsmodels').setLevel(logging.ERROR)

# === Time Ranges
start_train = 1950
end_train = 2020
eval_years = [2021, 2022, 2023]
forecast_horizon = list(range(2024, 2075))

# === Input Variables
selected_features_dict = {
    'Life expectancy': [
        'Child mortality rate' , 'GDP' , 'CPI_lag3' , 'Incomplete tertiary education_lag3' , 'Income_lag3' , 'Income',
        'CPI' , 'Inflation', 'Inflation_lag1', 'Cost of a healthy diet', 'Cost of a healthy diet_lag3' , 'Unemployment Rate_lag2',
        'Gini coefficient_lag3', 'Unemployment Rate_lag1'
    ],
    'Cardiovascular diseases': [
        'BMI_avg_lag3'
    ],

    'Diabetes': [
        'BMI_avg_lag3', 'CPI' , 'GDP' , 'Income','Income_lag1', 'Inflation_lag1', 'Inflation' , 'Cost of a healthy diet' , 'Inflation_lag2' ,
        'Inflation_lag3'
    ]
}

# === Ready Dataset (already loaded)
# df_forecast_ready = your real dataset

# === Forecasting and Evaluation
forecast_summary = []

for country in selected_countries:
    df_country = df_forecast_ready[df_forecast_ready['Country'] == country].sort_values('Year')

    for target in target_columns:
        if target not in df_country.columns:
            continue

        features = selected_features_dict.get(target, [])
        available_features = [f for f in features if f in df_country.columns]
        if not available_features:
            continue

        df_train = df_country[df_country['Year'].between(start_train, end_train)]
        df_eval = df_country[df_country['Year'].isin(eval_years)]
        df_forecast = df_country[df_country['Year'].isin(forecast_horizon)]
        actual_eval = df_eval[target].values

        #### ARIMA ####
        arima_rmse, arima_forecast = None, [None] * len(df_forecast)
        try:
            train_series = df_train[[target]].copy()
            train_series.index = pd.date_range(start='1950', periods=len(train_series), freq='YE')
            model = ARIMA(train_series, order=(1, 1, 1)).fit()
            pred_eval = model.predict(start=len(train_series), end=len(train_series) + len(df_eval) - 1)
            arima_rmse = np.sqrt(mean_squared_error(actual_eval, pred_eval))
            arima_forecast = model.predict(start=len(train_series) + len(df_eval),
                                           end=len(train_series) + len(df_eval) + len(df_forecast) - 1).tolist()
        except:
            pass

        #### Prophet ####
        prophet_rmse, prophet_forecast = None, [None] * len(df_forecast)
        try:
            prophet_df = df_train[['Year', target]].rename(columns={'Year': 'ds', target: 'y'})
            prophet_df['ds'] = pd.to_datetime(prophet_df['ds'], format='%Y')
            model = Prophet()
            model.fit(prophet_df)
            eval_dates = pd.DataFrame({'ds': pd.to_datetime(eval_years, format='%Y')})
            forecast_eval = model.predict(eval_dates)
            prophet_rmse = np.sqrt(mean_squared_error(actual_eval, forecast_eval['yhat'].values))
            forecast_years = pd.DataFrame({'ds': pd.to_datetime(df_forecast['Year'], format='%Y')})
            prophet_forecast = model.predict(forecast_years)['yhat'].tolist()
        except:
            pass

        #### Random Forest ####
        rf_rmse, rf_forecast = None, [None] * len(df_forecast)
        try:
            X = df_country[available_features]
            y = df_country[target]
            X_train = X[df_country['Year'].between(start_train, end_train)]
            y_train = y[df_country['Year'].between(start_train, end_train)]
            X_eval = X[df_country['Year'].isin(eval_years)]
            y_eval = y[df_country['Year'].isin(eval_years)]
            model = RandomForestRegressor(n_estimators=100, random_state=42)
            model.fit(X_train, y_train)
            pred_eval = model.predict(X_eval)
            rf_rmse = np.sqrt(mean_squared_error(y_eval, pred_eval))
            X_forecast = X[df_country['Year'].isin(forecast_horizon)]
            if not X_forecast.isnull().any(axis=1).any():
                rf_forecast = model.predict(X_forecast).tolist()
        except:
            pass

        for i, year in enumerate(df_forecast['Year']):
            forecast_summary.append({
                "Country": country,
                "Target": target,
                "Year": year,
                "ARIMA_RMSE": arima_rmse,
                "ARIMA_Forecast": arima_forecast[i],
                "Prophet_RMSE": prophet_rmse,
                "Prophet_Forecast": prophet_forecast[i],
                "RF_RMSE": rf_rmse,
                "RF_Forecast": rf_forecast[i]
            })

# === Combine All Results
df_model_comparison = pd.DataFrame(forecast_summary)

# === Summary Table: Best Model by RMSE
summary_table = df_model_comparison.groupby(['Country', 'Target'])[['ARIMA_RMSE', 'Prophet_RMSE', 'RF_RMSE']].first().reset_index()

def best_model_picker(row):
    scores = {
        'ARIMA': row['ARIMA_RMSE'],
        'Prophet': row['Prophet_RMSE'],
        'RF': row['RF_RMSE']
    }
    return min(scores, key=lambda k: scores[k] if pd.notnull(scores[k]) else np.inf)

summary_table['🎯 Best_Model'] = summary_table.apply(best_model_picker, axis=1)

# === Display Results
print("\n📊 Summary of Best Models per Country and Target:\n")
print(summary_table[['Country', 'Target', 'ARIMA_RMSE', 'Prophet_RMSE', 'RF_RMSE', '🎯 Best_Model']].to_string(index=False))

# === Optional Preview of Forecasts
sample_years = [2025, 2030, 2040, 2050, 2060, 2074]
df_sample = df_model_comparison[df_model_comparison['Year'].isin(sample_years)]
df_sample = df_sample.sort_values(['Country', 'Target', 'Year'])
print("\n📋 Forecasts for Selected Years:\n")
print(df_sample.head(30).to_string(index=False))

# Export summary
summary_table.to_csv("summary_table.csv", index=False)

# Download to your computer
from google.colab import files
files.download("summary_table.csv")

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DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=60372', 'data', 'file=/tmp/tmprjkocm4m/yick6axh.json', 'init=/tmp/tmprjkocm4m/tpmo8mfn.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelrbqjn69i/prophet_model-20250723142730.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/yy1ou4dl.json
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DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=65453', 'data', 'file=/tmp/tmprjkocm4m/yy1ou4dl.json', 'init=/tmp/tmprjkocm4m/3ufp0eys.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modeldjftl38l/prophet_model-20250723142731.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/alvi6xs0.json
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DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=18390', 'data', 'file=/tmp/tmprjkocm4m/alvi6xs0.json', 'init=/tmp/tmprjkocm4m/l135u0am.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_model7j6py8hp/prophet_model-20250723142732.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/6rvt9dt5.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/y7w05mpe.json
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DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=6064', 'data', 'file=/tmp/tmprjkocm4m/6rvt9dt5.json', 'init=/tmp/tmprjkocm4m/y7w05mpe.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelzx285i_x/prophet_model-20250723142733.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/6ws1o14o.json
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DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=57084', 'data', 'file=/tmp/tmprjkocm4m/6ws1o14o.json', 'init=/tmp/tmprjkocm4m/yj9xeaw5.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modeltfwe1azm/prophet_model-20250723142733.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/0_ha6_es.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/80y4z0qs.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=82127', 'data', 'file=/tmp/tmprjkocm4m/0_ha6_es.json', 'init=/tmp/tmprjkocm4m/80y4z0qs.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelhg28fgqr/prophet_model-20250723142734.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
14:27:34 - cmdstanpy - INFO - Chain [1] start processing
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/q7bqzno3.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/arav09wr.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=69149', 'data', 'file=/tmp/tmprjkocm4m/q7bqzno3.json', 'init=/tmp/tmprjkocm4m/arav09wr.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_model_0k_gwxi/prophet_model-20250723142735.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:cmdstanpy:Chain [1] start processing
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/a_wxfuz1.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/x1_dz2um.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=79623', 'data', 'file=/tmp/tmprjkocm4m/a_wxfuz1.json', 'init=/tmp/tmprjkocm4m/x1_dz2um.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_model3zsz2fsm/prophet_model-20250723142736.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
14:27:36 - cmdstanpy - INFO - Chain [1] start processing
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/2z3mu9uq.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/ghs_5hi3.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=80892', 'data', 'file=/tmp/tmprjkocm4m/2z3mu9uq.json', 'init=/tmp/tmprjkocm4m/ghs_5hi3.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelzmttl_kw/prophet_model-20250723142737.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/0czxr6n3.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/_n6wsd0y.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=6662', 'data', 'file=/tmp/tmprjkocm4m/0czxr6n3.json', 'init=/tmp/tmprjkocm4m/_n6wsd0y.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_model25eiolvn/prophet_model-20250723142737.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/c9lghnac.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/h21hf3u2.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=75262', 'data', 'file=/tmp/tmprjkocm4m/c9lghnac.json', 'init=/tmp/tmprjkocm4m/h21hf3u2.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modeleic9ik8d/prophet_model-20250723142738.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/0xko49k2.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/wbtnovqr.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=1437', 'data', 'file=/tmp/tmprjkocm4m/0xko49k2.json', 'init=/tmp/tmprjkocm4m/wbtnovqr.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelv5a_av8n/prophet_model-20250723142739.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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📊 Summary of Best Models per Country and Target:

      Country                  Target  ARIMA_RMSE  Prophet_RMSE   RF_RMSE 🎯 Best_Model
   Bangladesh Cardiovascular diseases    1.175582      6.991238  4.924493        ARIMA
   Bangladesh                Diabetes    0.000036      2.987844  0.101733        ARIMA
   Bangladesh         Life expectancy    2.312728      1.676697  2.298684      Prophet
       Brazil Cardiovascular diseases    1.819507      6.547227  3.512954        ARIMA
       Brazil                Diabetes    0.000000      0.186005  0.045713        ARIMA
       Brazil         Life expectancy    3.009573      2.189554  1.286215           RF
      Germany Cardiovascular diseases    0.433925      2.125500  0.950348        ARIMA
      Germany                Diabetes    0.000000      2.758175  0.000000        ARIMA
      Germany         Life expectancy    0.474573      0.612408  0.336656           RF
        India Cardiovascular diseases   19.662985     37.420988 47.551155        ARIMA
        India                Diabetes    0.019744      0.830592  0.001732           RF
        India         Life expectancy    1.973657      2.475751  2.190597        ARIMA
    Indonesia Cardiovascular diseases    8.486563      7.998086  0.097082           RF
    Indonesia                Diabetes    0.000000      0.712114  0.003464        ARIMA
    Indonesia         Life expectancy    1.887179      1.692886  1.644150           RF
        Japan Cardiovascular diseases    1.547668      7.688441  4.237571        ARIMA
        Japan                Diabetes    0.000000      1.841061  0.016166        ARIMA
        Japan         Life expectancy    0.638746      0.576474  0.319972           RF
        Kenya Cardiovascular diseases    0.121752      0.933468  0.799274        ARIMA
        Kenya                Diabetes    0.000379      3.479734  0.005196        ARIMA
        Kenya         Life expectancy    3.235337      1.670562  1.293366           RF
       Mexico Cardiovascular diseases    0.578806      0.843693  6.276441        ARIMA
       Mexico                Diabetes    0.000000      0.799705  0.412910        ARIMA
       Mexico         Life expectancy    6.224500      2.428620  2.490150      Prophet
      Nigeria Cardiovascular diseases    0.716350      4.498448  3.617701        ARIMA
      Nigeria                Diabetes    0.000000      0.140798  0.002708        ARIMA
      Nigeria         Life expectancy    0.700330      0.369290  1.244393      Prophet
United States Cardiovascular diseases    1.190369     11.974926 10.091925        ARIMA
United States                Diabetes    0.007983      0.489566  0.004000           RF
United States         Life expectancy    1.996910      1.561422  1.217660           RF

📋 Forecasts for Selected Years:

   Country                  Target  Year  ARIMA_RMSE  ARIMA_Forecast  Prophet_RMSE  Prophet_Forecast  RF_RMSE  RF_Forecast
Bangladesh Cardiovascular diseases  2025    1.175582       30.440474      6.991238         22.343115 4.924493    23.340201
Bangladesh Cardiovascular diseases  2030    1.175582       31.940794      6.991238         24.463361 4.924493    23.340201
Bangladesh Cardiovascular diseases  2040    1.175582       34.260180      6.991238         28.785528 4.924493    23.340201
Bangladesh Cardiovascular diseases  2050    1.175582       35.897214      6.991238         32.425885 4.924493    23.340201
Bangladesh Cardiovascular diseases  2060    1.175582       37.052640      6.991238         36.748052 4.924493    23.340201
Bangladesh                Diabetes  2025    0.000036        9.800033      2.987844          6.593908 0.101733     9.643000
Bangladesh                Diabetes  2030    0.000036        9.800026      2.987844          6.364059 0.101733     9.643000
Bangladesh                Diabetes  2040    0.000036        9.800027      2.987844          5.788692 0.101733     9.643000
Bangladesh                Diabetes  2050    0.000036        9.800027      2.987844          5.211562 0.101733     9.643000
Bangladesh                Diabetes  2060    0.000036        9.800027      2.987844          4.636195 0.101733     9.643000
Bangladesh         Life expectancy  2025    2.312728       71.671340      1.676697         76.010260 2.298684    71.741197
Bangladesh         Life expectancy  2030    2.312728       71.671358      1.676697         77.911348 2.298684    71.741197
Bangladesh         Life expectancy  2040    2.312728       71.671358      1.676697         83.766696 2.298684    71.741197
Bangladesh         Life expectancy  2050    2.312728       71.671358      1.676697         88.736171 2.298684    71.741197
Bangladesh         Life expectancy  2060    2.312728       71.671358      1.676697         94.591519 2.298684    71.741197
    Brazil Cardiovascular diseases  2025    1.819507       37.512576      6.547227         34.048560 3.512954    35.433321
    Brazil Cardiovascular diseases  2030    1.819507       38.026433      6.547227         37.240321 3.512954    35.433321
    Brazil Cardiovascular diseases  2040    1.819507       38.724635      6.547227         43.801768 3.512954    35.433321
    Brazil Cardiovascular diseases  2050    1.819507       39.136841      6.547227         49.309747 3.512954    35.433321
    Brazil Cardiovascular diseases  2060    1.819507       39.380199      6.547227         55.871194 3.512954    35.433321
    Brazil                Diabetes  2025    0.000000        8.300000      0.186005          8.233562 0.045713     8.348000
    Brazil                Diabetes  2030    0.000000        8.300000      0.186005          8.506609 0.045713     8.348000
    Brazil                Diabetes  2040    0.000000        8.300000      0.186005          8.908965 0.045713     8.348000
    Brazil                Diabetes  2050    0.000000        8.300000      0.186005          9.428119 0.045713     8.348000
    Brazil                Diabetes  2060    0.000000        8.300000      0.186005          9.830475 0.045713     8.348000
    Brazil         Life expectancy  2025    3.009573       69.422619      2.189554         77.316414 1.286215    73.905454
    Brazil         Life expectancy  2030    3.009573       65.412065      2.189554         78.606403 1.286215    73.905454
    Brazil         Life expectancy  2040    3.009573       59.751236      2.189554         81.065716 1.286215    73.905454
    Brazil         Life expectancy  2050    3.009573       56.227383      2.189554         83.708557 1.286215    73.905454
    Brazil         Life expectancy  2060    3.009573       54.033793      2.189554         86.167871 1.286215    73.905454

# Evaluation metrics (RMSE, MAPE, R²)

from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score

def calculate_metrics(actual, predicted):
    rmse = np.sqrt(mean_squared_error(actual, predicted))
    mae = mean_absolute_error(actual, predicted)
    r2 = r2_score(actual, predicted)
    mape = np.mean(np.abs((actual - predicted) / actual)) * 100
    return round(rmse, 4), round(mape, 2), round(r2, 4)

metrics_summary = []
eval_results = []  # Add this above your for-country loop to initialize the collector

# Evaluation years
eval_years = [2021, 2022, 2023]

for country in selected_countries:
    df_country = df_forecast_ready[df_forecast_ready['Country'] == country]

    for target in target_columns:
        if target not in df_country.columns:
            continue

        actual = df_country[df_country['Year'].isin(eval_years)][target].values

        # --- ARIMA ---
        try:
            train_series = df_country[df_country['Year'].between(1950, 2020)][[target]]
            train_series.index = pd.date_range(start='1950', periods=len(train_series), freq='YE')
            model_arima = ARIMA(train_series, order=(1, 1, 1)).fit()
            arima_pred = model_arima.predict(start=len(train_series), end=len(train_series)+len(eval_years)-1)
            arima_rmse, arima_mape, arima_r2 = calculate_metrics(actual, arima_pred)
            metrics_summary.append({
                "Country": country, "Target": target, "Model": "ARIMA",
                "RMSE": arima_rmse, "MAPE": arima_mape, "R²": arima_r2
            })
        except:
            pass

        # --- Prophet ---
        try:
            prophet_df = df_country[df_country['Year'].between(1950, 2020)][['Year', target]].rename(columns={'Year': 'ds', target: 'y'})
            prophet_df['ds'] = pd.to_datetime(prophet_df['ds'], format='%Y')
            model_prophet = Prophet()
            model_prophet.fit(prophet_df)
            future_eval = pd.DataFrame({'ds': pd.to_datetime(eval_years, format='%Y')})
            prophet_pred = model_prophet.predict(future_eval)['yhat'].values
            prophet_rmse, prophet_mape, prophet_r2 = calculate_metrics(actual, prophet_pred)
            metrics_summary.append({
                "Country": country, "Target": target, "Model": "Prophet",
                "RMSE": prophet_rmse, "MAPE": prophet_mape, "R²": prophet_r2
            })
        except:
            pass

        # --- Random Forest ---
        try:
            features = selected_features_dict.get(target, [])
            available = [f for f in features if f in df_country.columns]
            X = df_country[available]
            y = df_country[target]
            X_train = X[df_country['Year'].between(1950, 2020)]
            y_train = y[df_country['Year'].between(1950, 2020)]
            X_eval = X[df_country['Year'].isin(eval_years)]
            model_rf = RandomForestRegressor(n_estimators=100, random_state=42)
            model_rf.fit(X_train, y_train)
            rf_pred = model_rf.predict(X_eval)
            rf_rmse, rf_mape, rf_r2 = calculate_metrics(actual, rf_pred)

        # ✅ Add this block
            eval_rows = pd.DataFrame({
               "Country": [country] * len(eval_years),
               "Target": [target] * len(eval_years),
               "Year": eval_years,
               "Prediction": rf_pred,
               "Actual": actual
            })
            eval_results.append(eval_rows)

            metrics_summary.append({
                "Country": country, "Target": target, "Model": "Random Forest",
                "RMSE": rf_rmse, "MAPE": rf_mape, "R²": rf_r2
            })
        except:
            pass
df_eval_pred = pd.concat(eval_results, ignore_index=True)

def pick_best_model(group):
    return group.loc[group['RMSE'].idxmin(), 'Model']

# Convert to DataFrame
df_metrics = pd.DataFrame(metrics_summary)

# Sort it and assign it to df_metrics_sorted
df_metrics_sorted = df_metrics.sort_values(['Country', 'Target', 'Model']).reset_index(drop=True)

# Best model picker function
def pick_best_model(group):
    return group.loc[group['RMSE'].idxmin(), 'Model']

# Assign Best_Model using groupby and transform
df_metrics_sorted['Best_Model'] = df_metrics_sorted.groupby(['Country', 'Target'])['RMSE'].transform(
    lambda x: df_metrics_sorted.loc[x.idxmin(), 'Model']
)

# Display full table
print("\n🎯 Step 20: Evaluation Summary with Best Model\n")
print(df_metrics_sorted[['Country', 'Target', 'Model', 'RMSE', 'MAPE', 'R²', 'Best_Model']].to_string(index=False))

# Export summary
df_metrics_sorted.to_csv("df_metrics_sorted.csv", index=False)

# Download to your computer
from google.colab import files
files.download("df_metrics_sorted.csv")

INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/eqjjlcvx.json
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DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=59368', 'data', 'file=/tmp/tmprjkocm4m/eqjjlcvx.json', 'init=/tmp/tmprjkocm4m/z2w7pcm_.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelxyjgjdq5/prophet_model-20250723142739.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/vg5i6vka.json
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DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=75109', 'data', 'file=/tmp/tmprjkocm4m/vg5i6vka.json', 'init=/tmp/tmprjkocm4m/lmka780h.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelh7h91zgo/prophet_model-20250723142741.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/gmsvuhmu.json
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DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=19632', 'data', 'file=/tmp/tmprjkocm4m/gmsvuhmu.json', 'init=/tmp/tmprjkocm4m/0y0ma29h.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelkw7mltvl/prophet_model-20250723142742.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/smiy06t0.json
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DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=36038', 'data', 'file=/tmp/tmprjkocm4m/smiy06t0.json', 'init=/tmp/tmprjkocm4m/riq4u0nn.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelczq1p405/prophet_model-20250723142743.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/t008m8yn.json
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DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=99765', 'data', 'file=/tmp/tmprjkocm4m/t008m8yn.json', 'init=/tmp/tmprjkocm4m/6dwjfhiq.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelr_kg546m/prophet_model-20250723142744.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/oadqeb0v.json
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DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=12168', 'data', 'file=/tmp/tmprjkocm4m/oadqeb0v.json', 'init=/tmp/tmprjkocm4m/pk4m4u71.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelq7u60i3_/prophet_model-20250723142746.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/h40__qsi.json
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DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=57212', 'data', 'file=/tmp/tmprjkocm4m/h40__qsi.json', 'init=/tmp/tmprjkocm4m/n32vskud.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelz3ogozpi/prophet_model-20250723142747.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/gscmz__t.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/ik9qkw_0.json
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DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=25384', 'data', 'file=/tmp/tmprjkocm4m/gscmz__t.json', 'init=/tmp/tmprjkocm4m/ik9qkw_0.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modeldacp3582/prophet_model-20250723142749.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/7hpoi5td.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/zn3uklyn.json
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DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=93924', 'data', 'file=/tmp/tmprjkocm4m/7hpoi5td.json', 'init=/tmp/tmprjkocm4m/zn3uklyn.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_model0j5cee4e/prophet_model-20250723142749.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/8psco2ov.json
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DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=84665', 'data', 'file=/tmp/tmprjkocm4m/8psco2ov.json', 'init=/tmp/tmprjkocm4m/tkhr35wn.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modell9z44mqp/prophet_model-20250723142750.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/spgmtiay.json
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DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=3082', 'data', 'file=/tmp/tmprjkocm4m/spgmtiay.json', 'init=/tmp/tmprjkocm4m/io0cpspk.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelvtbox0en/prophet_model-20250723142751.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/eo_te0ht.json
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DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=60614', 'data', 'file=/tmp/tmprjkocm4m/eo_te0ht.json', 'init=/tmp/tmprjkocm4m/vc7l2mzf.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelchrkwy9s/prophet_model-20250723142752.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/pnk604ek.json
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DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=35287', 'data', 'file=/tmp/tmprjkocm4m/pnk604ek.json', 'init=/tmp/tmprjkocm4m/g732zgm_.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modeli6h6u0iz/prophet_model-20250723142752.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/b7vblsr6.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/fr2_eiu4.json
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DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=35398', 'data', 'file=/tmp/tmprjkocm4m/b7vblsr6.json', 'init=/tmp/tmprjkocm4m/fr2_eiu4.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelmhphwbs7/prophet_model-20250723142755.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/r9g89p_f.json
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DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=54075', 'data', 'file=/tmp/tmprjkocm4m/r9g89p_f.json', 'init=/tmp/tmprjkocm4m/ijnrdngf.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelnxglfmc3/prophet_model-20250723142756.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/ygbj5tne.json
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DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=54945', 'data', 'file=/tmp/tmprjkocm4m/ygbj5tne.json', 'init=/tmp/tmprjkocm4m/l9fgdivj.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelrv7d2fb0/prophet_model-20250723142756.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/9460n9e5.json
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DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=1255', 'data', 'file=/tmp/tmprjkocm4m/9460n9e5.json', 'init=/tmp/tmprjkocm4m/08bkdmuo.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_model_8o8l24m/prophet_model-20250723142757.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/8pdq6ykg.json
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DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=1282', 'data', 'file=/tmp/tmprjkocm4m/8pdq6ykg.json', 'init=/tmp/tmprjkocm4m/28lfdde0.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelhlgecqdp/prophet_model-20250723142758.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/tdpf1_54.json
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DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=38022', 'data', 'file=/tmp/tmprjkocm4m/tdpf1_54.json', 'init=/tmp/tmprjkocm4m/0dwo8sfe.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelzxjdv7s4/prophet_model-20250723142758.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/1p2t8ykc.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/vrwn2mde.json
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DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=28166', 'data', 'file=/tmp/tmprjkocm4m/1p2t8ykc.json', 'init=/tmp/tmprjkocm4m/vrwn2mde.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modeluzp444cl/prophet_model-20250723142759.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/njpwbnzq.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/kt8aa3fc.json
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DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=58212', 'data', 'file=/tmp/tmprjkocm4m/njpwbnzq.json', 'init=/tmp/tmprjkocm4m/kt8aa3fc.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_model92bmcut4/prophet_model-20250723142800.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/6_vkip_j.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/o8grblo9.json
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DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=4955', 'data', 'file=/tmp/tmprjkocm4m/6_vkip_j.json', 'init=/tmp/tmprjkocm4m/o8grblo9.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_model0i3bmw6j/prophet_model-20250723142800.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/lhv_eq_n.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/w9tg63fy.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=13290', 'data', 'file=/tmp/tmprjkocm4m/lhv_eq_n.json', 'init=/tmp/tmprjkocm4m/w9tg63fy.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_model1kzgl0y3/prophet_model-20250723142801.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/isyciumg.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/e5lx5x4_.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=7456', 'data', 'file=/tmp/tmprjkocm4m/isyciumg.json', 'init=/tmp/tmprjkocm4m/e5lx5x4_.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modeliabjxlt5/prophet_model-20250723142802.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/gfse9eex.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/p41bc0hj.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=18278', 'data', 'file=/tmp/tmprjkocm4m/gfse9eex.json', 'init=/tmp/tmprjkocm4m/p41bc0hj.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelasbnq6yo/prophet_model-20250723142802.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/vxqrg_hg.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/8m_ipt_q.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=62453', 'data', 'file=/tmp/tmprjkocm4m/vxqrg_hg.json', 'init=/tmp/tmprjkocm4m/8m_ipt_q.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelentrb2vk/prophet_model-20250723142803.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/twg124ht.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/w5quimbo.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=76939', 'data', 'file=/tmp/tmprjkocm4m/twg124ht.json', 'init=/tmp/tmprjkocm4m/w5quimbo.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modeljgyb62fy/prophet_model-20250723142804.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/t62344g0.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/tisx2xkx.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=79164', 'data', 'file=/tmp/tmprjkocm4m/t62344g0.json', 'init=/tmp/tmprjkocm4m/tisx2xkx.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_model39mmf_st/prophet_model-20250723142804.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:cmdstanpy:Chain [1] start processing
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INFO:cmdstanpy:Chain [1] done processing
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/b1h9unmc.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/hgcljw8a.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=47767', 'data', 'file=/tmp/tmprjkocm4m/b1h9unmc.json', 'init=/tmp/tmprjkocm4m/hgcljw8a.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelfey3cwl9/prophet_model-20250723142805.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:cmdstanpy:Chain [1] start processing
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INFO:cmdstanpy:Chain [1] done processing
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/adkcdrh2.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/evt78x3n.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=17956', 'data', 'file=/tmp/tmprjkocm4m/adkcdrh2.json', 'init=/tmp/tmprjkocm4m/evt78x3n.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelpg39k48t/prophet_model-20250723142806.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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🎯 Step 20: Evaluation Summary with Best Model

      Country                  Target         Model    RMSE  MAPE            R²    Best_Model
   Bangladesh Cardiovascular diseases         ARIMA  1.1756  4.02 -1.094927e+29         ARIMA
   Bangladesh Cardiovascular diseases       Prophet  6.9912 24.69 -3.872468e+30         ARIMA
   Bangladesh Cardiovascular diseases Random Forest  4.9245 17.42 -1.921333e+30         ARIMA
   Bangladesh                Diabetes         ARIMA  0.0000  0.00  0.000000e+00         ARIMA
   Bangladesh                Diabetes       Prophet  2.9878 30.49  0.000000e+00         ARIMA
   Bangladesh                Diabetes Random Forest  0.1017  0.81  0.000000e+00         ARIMA
   Bangladesh         Life expectancy         ARIMA  2.3127  2.76 -1.102500e+00       Prophet
   Bangladesh         Life expectancy       Prophet  1.6767  1.89 -1.051000e-01       Prophet
   Bangladesh         Life expectancy Random Forest  2.2987  2.94 -1.077100e+00       Prophet
       Brazil Cardiovascular diseases         ARIMA  1.8195  4.66  0.000000e+00         ARIMA
       Brazil Cardiovascular diseases       Prophet  6.5472 16.73  0.000000e+00         ARIMA
       Brazil Cardiovascular diseases Random Forest  3.5130  9.02  0.000000e+00         ARIMA
       Brazil                Diabetes         ARIMA  0.0000  0.00  1.000000e+00         ARIMA
       Brazil                Diabetes       Prophet  0.1860  2.14  0.000000e+00         ARIMA
       Brazil                Diabetes Random Forest  0.0457  0.55  0.000000e+00         ARIMA
       Brazil         Life expectancy         ARIMA  3.0096  3.29 -5.672800e+00 Random Forest
       Brazil         Life expectancy       Prophet  2.1896  2.66 -2.531900e+00 Random Forest
       Brazil         Life expectancy Random Forest  1.2862  1.52 -2.188000e-01 Random Forest
      Germany Cardiovascular diseases         ARIMA  0.4339  1.23  0.000000e+00         ARIMA
      Germany Cardiovascular diseases       Prophet  2.1255  5.82  0.000000e+00         ARIMA
      Germany Cardiovascular diseases Random Forest  0.9503  2.69  0.000000e+00         ARIMA
      Germany                Diabetes         ARIMA  0.0000  0.00  1.000000e+00         ARIMA
      Germany                Diabetes       Prophet  2.7582 55.13  0.000000e+00         ARIMA
      Germany                Diabetes Random Forest  0.0000  0.00  1.000000e+00         ARIMA
      Germany         Life expectancy         ARIMA  0.4746  0.44 -1.051900e+00 Random Forest
      Germany         Life expectancy       Prophet  0.6124  0.65 -2.417000e+00 Random Forest
      Germany         Life expectancy Random Forest  0.3367  0.38 -3.260000e-02 Random Forest
        India Cardiovascular diseases         ARIMA 19.6630  6.68  0.000000e+00         ARIMA
        India Cardiovascular diseases       Prophet 37.4210 12.75  0.000000e+00         ARIMA
        India Cardiovascular diseases Random Forest 47.5512 16.61  0.000000e+00         ARIMA
        India                Diabetes         ARIMA  0.0197  0.21  0.000000e+00 Random Forest
        India                Diabetes       Prophet  0.8306  9.49  0.000000e+00 Random Forest
        India                Diabetes Random Forest  0.0017  0.01  0.000000e+00 Random Forest
        India         Life expectancy         ARIMA  1.9737  2.25  1.628000e-01         ARIMA
        India         Life expectancy       Prophet  2.4758  2.42 -3.173000e-01         ARIMA
        India         Life expectancy Random Forest  2.1906  2.96 -3.130000e-02         ARIMA
    Indonesia Cardiovascular diseases         ARIMA  8.4866 11.75  0.000000e+00 Random Forest
    Indonesia Cardiovascular diseases       Prophet  7.9981  9.90  0.000000e+00 Random Forest
    Indonesia Cardiovascular diseases Random Forest  0.0971  0.13  0.000000e+00 Random Forest
    Indonesia                Diabetes         ARIMA  0.0000  0.00  1.000000e+00         ARIMA
    Indonesia                Diabetes       Prophet  0.7121  9.24  0.000000e+00         ARIMA
    Indonesia                Diabetes Random Forest  0.0035  0.03  0.000000e+00         ARIMA
    Indonesia         Life expectancy         ARIMA  1.8872  2.68 -2.444000e-01 Random Forest
    Indonesia         Life expectancy       Prophet  1.6929  1.48 -1.400000e-03 Random Forest
    Indonesia         Life expectancy Random Forest  1.6442  2.28  5.540000e-02 Random Forest
        Japan Cardiovascular diseases         ARIMA  1.5477  3.73  0.000000e+00         ARIMA
        Japan Cardiovascular diseases       Prophet  7.6884 18.56  0.000000e+00         ARIMA
        Japan Cardiovascular diseases Random Forest  4.2376 10.27  0.000000e+00         ARIMA
        Japan                Diabetes         ARIMA  0.0000  0.00  1.000000e+00         ARIMA
        Japan                Diabetes       Prophet  1.8411 27.47  0.000000e+00         ARIMA
        Japan                Diabetes Random Forest  0.0162  0.14  0.000000e+00         ARIMA
        Japan         Life expectancy         ARIMA  0.6387  0.68 -4.204900e+00 Random Forest
        Japan         Life expectancy       Prophet  0.5765  0.59 -3.239500e+00 Random Forest
        Japan         Life expectancy Random Forest  0.3200  0.37 -3.061000e-01 Random Forest
        Kenya Cardiovascular diseases         ARIMA  0.1218  3.48 -7.516462e+28         ARIMA
        Kenya Cardiovascular diseases       Prophet  0.9335 26.66 -4.418335e+30         ARIMA
        Kenya Cardiovascular diseases Random Forest  0.7993 22.85 -3.239297e+30         ARIMA
        Kenya                Diabetes         ARIMA  0.0004  0.01  0.000000e+00         ARIMA
        Kenya                Diabetes       Prophet  3.4797 57.98  0.000000e+00         ARIMA
        Kenya                Diabetes Random Forest  0.0052  0.08  0.000000e+00         ARIMA
        Kenya         Life expectancy         ARIMA  3.2353  4.35 -7.360000e+00 Random Forest
        Kenya         Life expectancy       Prophet  1.6706  2.25 -1.228900e+00 Random Forest
        Kenya         Life expectancy Random Forest  1.2934  1.95 -3.360000e-01 Random Forest
       Mexico Cardiovascular diseases         ARIMA  0.5788  2.17 -2.654270e+28         ARIMA
       Mexico Cardiovascular diseases       Prophet  0.8437  3.08 -5.639601e+28         ARIMA
       Mexico Cardiovascular diseases Random Forest  6.2764 28.37 -3.121092e+30         ARIMA
       Mexico                Diabetes         ARIMA  0.0000  0.00  1.000000e+00         ARIMA
       Mexico                Diabetes       Prophet  0.7997  7.13 -2.026747e+29         ARIMA
       Mexico                Diabetes Random Forest  0.4129  3.01 -5.403202e+28         ARIMA
       Mexico         Life expectancy         ARIMA  6.2245  7.05 -6.367500e+00       Prophet
       Mexico         Life expectancy       Prophet  2.4286  2.54 -1.216000e-01       Prophet
       Mexico         Life expectancy Random Forest  2.4902  3.34 -1.791000e-01       Prophet
      Nigeria Cardiovascular diseases         ARIMA  0.7164  3.98  0.000000e+00         ARIMA
      Nigeria Cardiovascular diseases       Prophet  4.4984 24.97  0.000000e+00         ARIMA
      Nigeria Cardiovascular diseases Random Forest  3.6177 20.11  0.000000e+00         ARIMA
      Nigeria                Diabetes         ARIMA  0.0000  0.00  1.000000e+00         ARIMA
      Nigeria                Diabetes       Prophet  0.1408  2.06  0.000000e+00         ARIMA
      Nigeria                Diabetes Random Forest  0.0027  0.04  0.000000e+00         ARIMA
      Nigeria         Life expectancy         ARIMA  0.7003  1.17 -1.846100e+00       Prophet
      Nigeria         Life expectancy       Prophet  0.3693  0.58  2.086000e-01       Prophet
      Nigeria         Life expectancy Random Forest  1.2444  2.14 -7.985900e+00       Prophet
United States Cardiovascular diseases         ARIMA  1.1904  1.29  0.000000e+00         ARIMA
United States Cardiovascular diseases       Prophet 11.9749 12.93  0.000000e+00         ARIMA
United States Cardiovascular diseases Random Forest 10.0919 10.96  0.000000e+00         ARIMA
United States                Diabetes         ARIMA  0.0080  0.10  0.000000e+00 Random Forest
United States                Diabetes       Prophet  0.4896  6.65  0.000000e+00 Random Forest
United States                Diabetes Random Forest  0.0040  0.05  0.000000e+00 Random Forest
United States         Life expectancy         ARIMA  1.9969  2.10 -1.796800e+00 Random Forest
United States         Life expectancy       Prophet  1.5614  1.63 -7.100000e-01 Random Forest
United States         Life expectancy Random Forest  1.2177  1.39 -3.990000e-02 Random Forest

# Evaluation metrics (RMSE, MAPE, R²)

from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score

def calculate_metrics(actual, predicted):
    rmse = np.sqrt(mean_squared_error(actual, predicted))
    mae = mean_absolute_error(actual, predicted)
    r2 = r2_score(actual, predicted)
    mape = np.mean(np.abs((actual - predicted) / actual)) * 100
    return round(rmse, 4), round(mape, 2), round(r2, 4)

metrics_summary = []

# Evaluation years
eval_years = [2021, 2022, 2023]

for country in selected_countries:
    df_country = df_forecast_ready[df_forecast_ready['Country'] == country]

    for target in target_columns:
        if target not in df_country.columns:
            continue

        actual = df_country[df_country['Year'].isin(eval_years)][target].values

        # --- ARIMA ---
        try:
            train_series = df_country[df_country['Year'].between(1950, 2020)][[target]]
            train_series.index = pd.date_range(start='1950', periods=len(train_series), freq='YE')
            model_arima = ARIMA(train_series, order=(1, 1, 1)).fit()
            arima_pred = model_arima.predict(start=len(train_series), end=len(train_series)+len(eval_years)-1)
            arima_rmse, arima_mape, arima_r2 = calculate_metrics(actual, arima_pred)
            metrics_summary.append({
                "Country": country, "Target": target, "Model": "ARIMA",
                "RMSE": arima_rmse, "MAPE": arima_mape, "R²": arima_r2
            })
        except:
            pass

        # --- Prophet ---
        try:
            prophet_df = df_country[df_country['Year'].between(1950, 2020)][['Year', target]].rename(columns={'Year': 'ds', target: 'y'})
            prophet_df['ds'] = pd.to_datetime(prophet_df['ds'], format='%Y')
            model_prophet = Prophet()
            model_prophet.fit(prophet_df)
            future_eval = pd.DataFrame({'ds': pd.to_datetime(eval_years, format='%Y')})
            prophet_pred = model_prophet.predict(future_eval)['yhat'].values
            prophet_rmse, prophet_mape, prophet_r2 = calculate_metrics(actual, prophet_pred)
            metrics_summary.append({
                "Country": country, "Target": target, "Model": "Prophet",
                "RMSE": prophet_rmse, "MAPE": prophet_mape, "R²": prophet_r2
            })
        except:
            pass

        # --- Random Forest ---
        try:
            features = selected_features_dict.get(target, [])
            available = [f for f in features if f in df_country.columns]
            X = df_country[available]
            y = df_country[target]
            X_train = X[df_country['Year'].between(1950, 2020)]
            y_train = y[df_country['Year'].between(1950, 2020)]
            X_eval = X[df_country['Year'].isin(eval_years)]
            model_rf = RandomForestRegressor(n_estimators=100, random_state=42)
            model_rf.fit(X_train, y_train)
            rf_pred = model_rf.predict(X_eval)
            rf_rmse, rf_mape, rf_r2 = calculate_metrics(actual, rf_pred)
            metrics_summary.append({
                "Country": country, "Target": target, "Model": "Random Forest",
                "RMSE": rf_rmse, "MAPE": rf_mape, "R²": rf_r2
            })
        except:
            pass

def pick_best_model(group):
    return group.loc[group['RMSE'].idxmin(), 'Model']

# Convert to DataFrame
df_metrics = pd.DataFrame(metrics_summary)

# Sort it and assign it to df_metrics_sorted
df_metrics_sorted = df_metrics.sort_values(['Country', 'Target', 'Model']).reset_index(drop=True)

# Best model picker function
def pick_best_model(group):
    return group.loc[group['RMSE'].idxmin(), 'Model']

# Assign Best_Model using groupby and transform
df_metrics_sorted['Best_Model'] = df_metrics_sorted.groupby(['Country', 'Target'])['RMSE'].transform(
    lambda x: df_metrics_sorted.loc[x.idxmin(), 'Model']
)

# Display full table
print("\n🎯 Step 20: Evaluation Summary with Best Model\n")
print(df_metrics_sorted[['Country', 'Target', 'Model', 'RMSE', 'MAPE', 'R²', 'Best_Model']].to_string(index=False))

INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/96agqxb4.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/a63jie00.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=58230', 'data', 'file=/tmp/tmprjkocm4m/96agqxb4.json', 'init=/tmp/tmprjkocm4m/a63jie00.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelrbxqmd3k/prophet_model-20250723142811.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
14:28:11 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
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INFO:cmdstanpy:Chain [1] done processing
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/f7yn6mcf.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/vekzyhes.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=5483', 'data', 'file=/tmp/tmprjkocm4m/f7yn6mcf.json', 'init=/tmp/tmprjkocm4m/vekzyhes.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelutxoc49d/prophet_model-20250723142812.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
14:28:12 - cmdstanpy - INFO - Chain [1] start processing
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/56jfux6d.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/nxnbae66.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=42415', 'data', 'file=/tmp/tmprjkocm4m/56jfux6d.json', 'init=/tmp/tmprjkocm4m/nxnbae66.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_model78j27m6s/prophet_model-20250723142812.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
14:28:12 - cmdstanpy - INFO - Chain [1] start processing
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/9ph1dk0k.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/to_q09k2.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=80779', 'data', 'file=/tmp/tmprjkocm4m/9ph1dk0k.json', 'init=/tmp/tmprjkocm4m/to_q09k2.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_model3wcrj73h/prophet_model-20250723142813.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
14:28:13 - cmdstanpy - INFO - Chain [1] start processing
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/8lcv5y6z.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/kjesknro.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=15638', 'data', 'file=/tmp/tmprjkocm4m/8lcv5y6z.json', 'init=/tmp/tmprjkocm4m/kjesknro.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelcgb49lbt/prophet_model-20250723142814.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
14:28:14 - cmdstanpy - INFO - Chain [1] start processing
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/75qvkpfx.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/i2i8_56b.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=7959', 'data', 'file=/tmp/tmprjkocm4m/75qvkpfx.json', 'init=/tmp/tmprjkocm4m/i2i8_56b.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelp1rbzqxl/prophet_model-20250723142815.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
14:28:15 - cmdstanpy - INFO - Chain [1] start processing
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/35m11l02.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/2cf1fb8e.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=45962', 'data', 'file=/tmp/tmprjkocm4m/35m11l02.json', 'init=/tmp/tmprjkocm4m/2cf1fb8e.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelpjdmugoc/prophet_model-20250723142816.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
14:28:16 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/6b03wzex.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/r6bgqq_5.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=36355', 'data', 'file=/tmp/tmprjkocm4m/6b03wzex.json', 'init=/tmp/tmprjkocm4m/r6bgqq_5.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_model01dxs0d2/prophet_model-20250723142818.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
14:28:18 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
14:28:19 - cmdstanpy - INFO - Chain [1] done processing
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/h_0isjxz.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/4666mwro.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=50895', 'data', 'file=/tmp/tmprjkocm4m/h_0isjxz.json', 'init=/tmp/tmprjkocm4m/4666mwro.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelujssn5hl/prophet_model-20250723142820.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
14:28:20 - cmdstanpy - INFO - Chain [1] start processing
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/q5094zuy.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/d21_bqlg.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=42067', 'data', 'file=/tmp/tmprjkocm4m/q5094zuy.json', 'init=/tmp/tmprjkocm4m/d21_bqlg.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelavpi2gqq/prophet_model-20250723142822.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
14:28:22 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/t5p7euuw.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/4lqozamx.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=47422', 'data', 'file=/tmp/tmprjkocm4m/t5p7euuw.json', 'init=/tmp/tmprjkocm4m/4lqozamx.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelygoqwes7/prophet_model-20250723142823.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
14:28:23 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/tzl8hmsg.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/aqj2c0s9.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=54955', 'data', 'file=/tmp/tmprjkocm4m/tzl8hmsg.json', 'init=/tmp/tmprjkocm4m/aqj2c0s9.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_model34c4yai4/prophet_model-20250723142824.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
14:28:24 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
14:28:25 - cmdstanpy - INFO - Chain [1] done processing
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/1jdjv1ym.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/5ply91ys.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=16233', 'data', 'file=/tmp/tmprjkocm4m/1jdjv1ym.json', 'init=/tmp/tmprjkocm4m/5ply91ys.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modeln8xdotuh/prophet_model-20250723142825.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
14:28:25 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/tg6xi57o.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/em7pt54s.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=82535', 'data', 'file=/tmp/tmprjkocm4m/tg6xi57o.json', 'init=/tmp/tmprjkocm4m/em7pt54s.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modellbr_d7wm/prophet_model-20250723142827.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
14:28:27 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
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INFO:cmdstanpy:Chain [1] done processing
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/56s2q0ui.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/gy08iedo.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=60748', 'data', 'file=/tmp/tmprjkocm4m/56s2q0ui.json', 'init=/tmp/tmprjkocm4m/gy08iedo.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelg7jfvios/prophet_model-20250723142828.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
14:28:28 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
14:28:28 - cmdstanpy - INFO - Chain [1] done processing
INFO:cmdstanpy:Chain [1] done processing
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/mu2y1iux.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/v7jgd4r2.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=9337', 'data', 'file=/tmp/tmprjkocm4m/mu2y1iux.json', 'init=/tmp/tmprjkocm4m/v7jgd4r2.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelku5nak1k/prophet_model-20250723142829.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
14:28:29 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
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INFO:cmdstanpy:Chain [1] done processing
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/jzvyvewp.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/mr48mr14.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=23976', 'data', 'file=/tmp/tmprjkocm4m/jzvyvewp.json', 'init=/tmp/tmprjkocm4m/mr48mr14.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_model3lpo3ui7/prophet_model-20250723142829.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
14:28:29 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
14:28:30 - cmdstanpy - INFO - Chain [1] done processing
INFO:cmdstanpy:Chain [1] done processing
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/ofu8h322.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/k8n5hxd9.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=70178', 'data', 'file=/tmp/tmprjkocm4m/ofu8h322.json', 'init=/tmp/tmprjkocm4m/k8n5hxd9.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelxh05y0r4/prophet_model-20250723142830.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
14:28:30 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
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INFO:cmdstanpy:Chain [1] done processing
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/g2xiz9hi.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/t5g5emio.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=22075', 'data', 'file=/tmp/tmprjkocm4m/g2xiz9hi.json', 'init=/tmp/tmprjkocm4m/t5g5emio.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_model8cduqjex/prophet_model-20250723142831.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
14:28:31 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
14:28:31 - cmdstanpy - INFO - Chain [1] done processing
INFO:cmdstanpy:Chain [1] done processing
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/m0rwqk4l.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/xnpt149d.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=77601', 'data', 'file=/tmp/tmprjkocm4m/m0rwqk4l.json', 'init=/tmp/tmprjkocm4m/xnpt149d.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modeldubr4w1q/prophet_model-20250723142831.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
14:28:31 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
14:28:32 - cmdstanpy - INFO - Chain [1] done processing
INFO:cmdstanpy:Chain [1] done processing
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/e2it4bm5.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/jh2zfokd.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=61228', 'data', 'file=/tmp/tmprjkocm4m/e2it4bm5.json', 'init=/tmp/tmprjkocm4m/jh2zfokd.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelhr6l8_bh/prophet_model-20250723142833.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
14:28:33 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
14:28:33 - cmdstanpy - INFO - Chain [1] done processing
INFO:cmdstanpy:Chain [1] done processing
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/lnzx2fbb.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/ledcbnmk.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=22655', 'data', 'file=/tmp/tmprjkocm4m/lnzx2fbb.json', 'init=/tmp/tmprjkocm4m/ledcbnmk.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modeld9g72k_0/prophet_model-20250723142834.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
14:28:34 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
14:28:35 - cmdstanpy - INFO - Chain [1] done processing
INFO:cmdstanpy:Chain [1] done processing
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/abygc97f.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/3vxzg627.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=41273', 'data', 'file=/tmp/tmprjkocm4m/abygc97f.json', 'init=/tmp/tmprjkocm4m/3vxzg627.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelpq_jed9k/prophet_model-20250723142835.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
14:28:35 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
14:28:35 - cmdstanpy - INFO - Chain [1] done processing
INFO:cmdstanpy:Chain [1] done processing
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/pwjgxwnc.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/qvr8h8ur.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=51176', 'data', 'file=/tmp/tmprjkocm4m/pwjgxwnc.json', 'init=/tmp/tmprjkocm4m/qvr8h8ur.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelukr7_ca3/prophet_model-20250723142835.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
14:28:35 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
14:28:36 - cmdstanpy - INFO - Chain [1] done processing
INFO:cmdstanpy:Chain [1] done processing
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/0e4wunt5.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/x3p3rsx4.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=22674', 'data', 'file=/tmp/tmprjkocm4m/0e4wunt5.json', 'init=/tmp/tmprjkocm4m/x3p3rsx4.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelzbgkl07q/prophet_model-20250723142836.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
14:28:36 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
14:28:37 - cmdstanpy - INFO - Chain [1] done processing
INFO:cmdstanpy:Chain [1] done processing
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/lbc7c6mv.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/msjjsc5w.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=91260', 'data', 'file=/tmp/tmprjkocm4m/lbc7c6mv.json', 'init=/tmp/tmprjkocm4m/msjjsc5w.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_model17vi9bbj/prophet_model-20250723142837.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
14:28:37 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
14:28:37 - cmdstanpy - INFO - Chain [1] done processing
INFO:cmdstanpy:Chain [1] done processing
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/qjasgdr_.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/u1fq__1x.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=81508', 'data', 'file=/tmp/tmprjkocm4m/qjasgdr_.json', 'init=/tmp/tmprjkocm4m/u1fq__1x.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_model5d4_6ltf/prophet_model-20250723142838.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
14:28:38 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
14:28:38 - cmdstanpy - INFO - Chain [1] done processing
INFO:cmdstanpy:Chain [1] done processing
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/0zuanv8u.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/j0tr_zru.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=72026', 'data', 'file=/tmp/tmprjkocm4m/0zuanv8u.json', 'init=/tmp/tmprjkocm4m/j0tr_zru.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_model4fjx7_g4/prophet_model-20250723142838.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
14:28:38 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
14:28:39 - cmdstanpy - INFO - Chain [1] done processing
INFO:cmdstanpy:Chain [1] done processing
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/m8l9oi1q.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/fosm3gdg.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=75488', 'data', 'file=/tmp/tmprjkocm4m/m8l9oi1q.json', 'init=/tmp/tmprjkocm4m/fosm3gdg.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelit1ljs_j/prophet_model-20250723142839.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
14:28:39 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
14:28:39 - cmdstanpy - INFO - Chain [1] done processing
INFO:cmdstanpy:Chain [1] done processing
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/vdfsimm5.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/559dtkca.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=38508', 'data', 'file=/tmp/tmprjkocm4m/vdfsimm5.json', 'init=/tmp/tmprjkocm4m/559dtkca.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_model2fwcy8wp/prophet_model-20250723142839.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
14:28:39 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
14:28:40 - cmdstanpy - INFO - Chain [1] done processing
INFO:cmdstanpy:Chain [1] done processing

🎯 Step 20: Evaluation Summary with Best Model

      Country                  Target         Model    RMSE  MAPE            R²    Best_Model
   Bangladesh Cardiovascular diseases         ARIMA  1.1756  4.02 -1.094927e+29         ARIMA
   Bangladesh Cardiovascular diseases       Prophet  6.9912 24.69 -3.872468e+30         ARIMA
   Bangladesh Cardiovascular diseases Random Forest  4.9245 17.42 -1.921333e+30         ARIMA
   Bangladesh                Diabetes         ARIMA  0.0000  0.00  0.000000e+00         ARIMA
   Bangladesh                Diabetes       Prophet  2.9878 30.49  0.000000e+00         ARIMA
   Bangladesh                Diabetes Random Forest  0.1017  0.81  0.000000e+00         ARIMA
   Bangladesh         Life expectancy         ARIMA  2.3127  2.76 -1.102500e+00       Prophet
   Bangladesh         Life expectancy       Prophet  1.6767  1.89 -1.051000e-01       Prophet
   Bangladesh         Life expectancy Random Forest  2.2987  2.94 -1.077100e+00       Prophet
       Brazil Cardiovascular diseases         ARIMA  1.8195  4.66  0.000000e+00         ARIMA
       Brazil Cardiovascular diseases       Prophet  6.5472 16.73  0.000000e+00         ARIMA
       Brazil Cardiovascular diseases Random Forest  3.5130  9.02  0.000000e+00         ARIMA
       Brazil                Diabetes         ARIMA  0.0000  0.00  1.000000e+00         ARIMA
       Brazil                Diabetes       Prophet  0.1860  2.14  0.000000e+00         ARIMA
       Brazil                Diabetes Random Forest  0.0457  0.55  0.000000e+00         ARIMA
       Brazil         Life expectancy         ARIMA  3.0096  3.29 -5.672800e+00 Random Forest
       Brazil         Life expectancy       Prophet  2.1896  2.66 -2.531900e+00 Random Forest
       Brazil         Life expectancy Random Forest  1.2862  1.52 -2.188000e-01 Random Forest
      Germany Cardiovascular diseases         ARIMA  0.4339  1.23  0.000000e+00         ARIMA
      Germany Cardiovascular diseases       Prophet  2.1255  5.82  0.000000e+00         ARIMA
      Germany Cardiovascular diseases Random Forest  0.9503  2.69  0.000000e+00         ARIMA
      Germany                Diabetes         ARIMA  0.0000  0.00  1.000000e+00         ARIMA
      Germany                Diabetes       Prophet  2.7582 55.13  0.000000e+00         ARIMA
      Germany                Diabetes Random Forest  0.0000  0.00  1.000000e+00         ARIMA
      Germany         Life expectancy         ARIMA  0.4746  0.44 -1.051900e+00 Random Forest
      Germany         Life expectancy       Prophet  0.6124  0.65 -2.417000e+00 Random Forest
      Germany         Life expectancy Random Forest  0.3367  0.38 -3.260000e-02 Random Forest
        India Cardiovascular diseases         ARIMA 19.6630  6.68  0.000000e+00         ARIMA
        India Cardiovascular diseases       Prophet 37.4210 12.75  0.000000e+00         ARIMA
        India Cardiovascular diseases Random Forest 47.5512 16.61  0.000000e+00         ARIMA
        India                Diabetes         ARIMA  0.0197  0.21  0.000000e+00 Random Forest
        India                Diabetes       Prophet  0.8306  9.49  0.000000e+00 Random Forest
        India                Diabetes Random Forest  0.0017  0.01  0.000000e+00 Random Forest
        India         Life expectancy         ARIMA  1.9737  2.25  1.628000e-01         ARIMA
        India         Life expectancy       Prophet  2.4758  2.42 -3.173000e-01         ARIMA
        India         Life expectancy Random Forest  2.1906  2.96 -3.130000e-02         ARIMA
    Indonesia Cardiovascular diseases         ARIMA  8.4866 11.75  0.000000e+00 Random Forest
    Indonesia Cardiovascular diseases       Prophet  7.9981  9.90  0.000000e+00 Random Forest
    Indonesia Cardiovascular diseases Random Forest  0.0971  0.13  0.000000e+00 Random Forest
    Indonesia                Diabetes         ARIMA  0.0000  0.00  1.000000e+00         ARIMA
    Indonesia                Diabetes       Prophet  0.7121  9.24  0.000000e+00         ARIMA
    Indonesia                Diabetes Random Forest  0.0035  0.03  0.000000e+00         ARIMA
    Indonesia         Life expectancy         ARIMA  1.8872  2.68 -2.444000e-01 Random Forest
    Indonesia         Life expectancy       Prophet  1.6929  1.48 -1.400000e-03 Random Forest
    Indonesia         Life expectancy Random Forest  1.6442  2.28  5.540000e-02 Random Forest
        Japan Cardiovascular diseases         ARIMA  1.5477  3.73  0.000000e+00         ARIMA
        Japan Cardiovascular diseases       Prophet  7.6884 18.56  0.000000e+00         ARIMA
        Japan Cardiovascular diseases Random Forest  4.2376 10.27  0.000000e+00         ARIMA
        Japan                Diabetes         ARIMA  0.0000  0.00  1.000000e+00         ARIMA
        Japan                Diabetes       Prophet  1.8411 27.47  0.000000e+00         ARIMA
        Japan                Diabetes Random Forest  0.0162  0.14  0.000000e+00         ARIMA
        Japan         Life expectancy         ARIMA  0.6387  0.68 -4.204900e+00 Random Forest
        Japan         Life expectancy       Prophet  0.5765  0.59 -3.239500e+00 Random Forest
        Japan         Life expectancy Random Forest  0.3200  0.37 -3.061000e-01 Random Forest
        Kenya Cardiovascular diseases         ARIMA  0.1218  3.48 -7.516462e+28         ARIMA
        Kenya Cardiovascular diseases       Prophet  0.9335 26.66 -4.418335e+30         ARIMA
        Kenya Cardiovascular diseases Random Forest  0.7993 22.85 -3.239297e+30         ARIMA
        Kenya                Diabetes         ARIMA  0.0004  0.01  0.000000e+00         ARIMA
        Kenya                Diabetes       Prophet  3.4797 57.98  0.000000e+00         ARIMA
        Kenya                Diabetes Random Forest  0.0052  0.08  0.000000e+00         ARIMA
        Kenya         Life expectancy         ARIMA  3.2353  4.35 -7.360000e+00 Random Forest
        Kenya         Life expectancy       Prophet  1.6706  2.25 -1.228900e+00 Random Forest
        Kenya         Life expectancy Random Forest  1.2934  1.95 -3.360000e-01 Random Forest
       Mexico Cardiovascular diseases         ARIMA  0.5788  2.17 -2.654270e+28         ARIMA
       Mexico Cardiovascular diseases       Prophet  0.8437  3.08 -5.639601e+28         ARIMA
       Mexico Cardiovascular diseases Random Forest  6.2764 28.37 -3.121092e+30         ARIMA
       Mexico                Diabetes         ARIMA  0.0000  0.00  1.000000e+00         ARIMA
       Mexico                Diabetes       Prophet  0.7997  7.13 -2.026747e+29         ARIMA
       Mexico                Diabetes Random Forest  0.4129  3.01 -5.403202e+28         ARIMA
       Mexico         Life expectancy         ARIMA  6.2245  7.05 -6.367500e+00       Prophet
       Mexico         Life expectancy       Prophet  2.4286  2.54 -1.216000e-01       Prophet
       Mexico         Life expectancy Random Forest  2.4902  3.34 -1.791000e-01       Prophet
      Nigeria Cardiovascular diseases         ARIMA  0.7164  3.98  0.000000e+00         ARIMA
      Nigeria Cardiovascular diseases       Prophet  4.4984 24.97  0.000000e+00         ARIMA
      Nigeria Cardiovascular diseases Random Forest  3.6177 20.11  0.000000e+00         ARIMA
      Nigeria                Diabetes         ARIMA  0.0000  0.00  1.000000e+00         ARIMA
      Nigeria                Diabetes       Prophet  0.1408  2.06  0.000000e+00         ARIMA
      Nigeria                Diabetes Random Forest  0.0027  0.04  0.000000e+00         ARIMA
      Nigeria         Life expectancy         ARIMA  0.7003  1.17 -1.846100e+00       Prophet
      Nigeria         Life expectancy       Prophet  0.3693  0.58  2.086000e-01       Prophet
      Nigeria         Life expectancy Random Forest  1.2444  2.14 -7.985900e+00       Prophet
United States Cardiovascular diseases         ARIMA  1.1904  1.29  0.000000e+00         ARIMA
United States Cardiovascular diseases       Prophet 11.9749 12.93  0.000000e+00         ARIMA
United States Cardiovascular diseases Random Forest 10.0919 10.96  0.000000e+00         ARIMA
United States                Diabetes         ARIMA  0.0080  0.10  0.000000e+00 Random Forest
United States                Diabetes       Prophet  0.4896  6.65  0.000000e+00 Random Forest
United States                Diabetes Random Forest  0.0040  0.05  0.000000e+00 Random Forest
United States         Life expectancy         ARIMA  1.9969  2.10 -1.796800e+00 Random Forest
United States         Life expectancy       Prophet  1.5614  1.63 -7.100000e-01 Random Forest
United States         Life expectancy Random Forest  1.2177  1.39 -3.990000e-02 Random Forest

# Plot Acutal vs Predict (RF, ARIMA, Prophet )

# Plots Testing- Actual vs Predict (RF, ARIMA, Prophet) -    18 July

import matplotlib.pyplot as plt
import numpy as np

def plot_target_forecast(df, country, target):
    # Filter data for country & target
    df_ct = df[(df['Country'] == country) & (df['Target'] == target)].sort_values('Year')

    # Extract data
    years = df_ct['Year']
    arima = df_ct['ARIMA_Forecast']
    rf = df_ct['RF_Forecast']
    prophet = df_ct['Prophet_Forecast']

    # Actual years
    actual_years = [2021, 2022, 2023]
    forecast_years = list(range(2024, 2075))
    actual_mask = df_ct['Year'].isin(actual_years)

    # Use ARIMA prediction as proxy for observed if needed
    actual_vals = arima[actual_mask]

    # Start plot
    plt.figure(figsize=(13, 6))

    # Forecast region shading
    plt.axvspan(2024, 2074, color='gray', alpha=0.12, label='Forecast Horizon')

    # Plot forecasts
    plt.plot(years, arima, color='forestgreen', linewidth=2, label='ARIMA Forecast')
    plt.plot(years, prophet, color='darkorchid', linewidth=2, label='Prophet Forecast')
    plt.plot(years, rf, color='navy', linewidth=2, label='Random Forest Forecast')

    # Plot actual values
    plt.scatter(df_ct.loc[actual_mask, 'Year'], actual_vals,
                color='orange', edgecolor='black', s=90,
                label='Observed (2021–2023)', zorder=5)

    # Final touches
    plt.title(f"{target} Forecast — {country}", fontsize=16)
    plt.xlabel("Year")
    plt.ylabel("Value")
    plt.grid(True)
    plt.legend()
    plt.tight_layout()
    plt.show()

    selected_countries = [
    'United States', 'Germany', 'Japan', 'Brazil', 'India',
    'Indonesia', 'Nigeria', 'Kenya', 'Mexico', 'Bangladesh'
]

for country in selected_countries:
    for target in ["Life expectancy", "Diabetes", "Cardiovascular diseases"]:
        plot_target_forecast(df_model_comparison, country, target)

# Plots Testing- Actual vs Predict (RF, ARIMA, Prophet) -    18 July  - TESTING 2

forecast_summary = []

for country in selected_countries:
    df_country = df_forecast_ready[df_forecast_ready['Country'] == country].sort_values('Year')

    for target in target_columns:
        if target not in df_country.columns:
            continue

        features = selected_features_dict.get(target, [])
        available_features = [f for f in features if f in df_country.columns]
        if not available_features:
            continue

        df_train = df_country[df_country['Year'].between(start_train, end_train)]
        df_eval = df_country[df_country['Year'].isin(eval_years)]
        df_forecast = df_country[df_country['Year'].isin(forecast_horizon)]
        actual_eval = df_eval[target].values

        #### ARIMA ####
        arima_rmse, arima_forecast_eval, arima_forecast = None, [], []
        try:
            train_series = df_train[[target]].copy()
            train_series.index = pd.date_range(start='1950', periods=len(train_series), freq='YE')
            model = ARIMA(train_series, order=(1, 1, 1)).fit()
            pred_eval_arima = model.predict(start=len(train_series), end=len(train_series)+len(df_eval)-1)
            arima_rmse = np.sqrt(mean_squared_error(actual_eval, pred_eval_arima))
            arima_forecast_eval = pred_eval_arima.tolist()
            arima_forecast = model.predict(start=len(train_series)+len(df_eval),
                                           end=len(train_series)+len(df_eval)+len(df_forecast)-1).tolist()
        except:
            pass

        #### Prophet ####
        prophet_rmse, prophet_forecast_eval, prophet_forecast = None, [], []
        try:
            prophet_df = df_train[['Year', target]].rename(columns={'Year': 'ds', target: 'y'})
            prophet_df['ds'] = pd.to_datetime(prophet_df['ds'], format='%Y')
            model = Prophet()
            model.fit(prophet_df)
            eval_dates = pd.DataFrame({'ds': pd.to_datetime(eval_years, format='%Y')})
            forecast_eval_prophet = model.predict(eval_dates)
            prophet_rmse = np.sqrt(mean_squared_error(actual_eval, forecast_eval_prophet['yhat'].values))
            prophet_forecast_eval = forecast_eval_prophet['yhat'].tolist()
            forecast_years_df = pd.DataFrame({'ds': pd.to_datetime(df_forecast['Year'], format='%Y')})
            prophet_forecast = model.predict(forecast_years_df)['yhat'].tolist()
        except:
            pass

        #### RF ####
        rf_rmse, rf_forecast_eval, rf_forecast = None, [], []
        try:
            X = df_country[available_features]
            y = df_country[target]
            X_train = X[df_country['Year'].between(start_train, end_train)]
            y_train = y[df_country['Year'].between(start_train, end_train)]
            X_eval = X[df_country['Year'].isin(eval_years)]
            y_eval = y[df_country['Year'].isin(eval_years)]
            model = RandomForestRegressor(n_estimators=100, random_state=42)
            model.fit(X_train, y_train)
            pred_eval_rf = model.predict(X_eval)
            rf_rmse = np.sqrt(mean_squared_error(y_eval, pred_eval_rf))
            rf_forecast_eval = pred_eval_rf.tolist()
            X_forecast = X[df_country['Year'].isin(forecast_horizon)]
            rf_forecast = model.predict(X_forecast).tolist() if not X_forecast.isnull().any(axis=1).any() else [None]*len(X_forecast)
        except:
            pass

        # Append evaluation predictions
        for i, year in enumerate(eval_years):
            forecast_summary.append({
                "Country": country,
                "Target": target,
                "Year": year,
                "ARIMA_RMSE": arima_rmse,
                "ARIMA_Forecast": arima_forecast_eval[i] if i < len(arima_forecast_eval) else None,
                "Prophet_RMSE": prophet_rmse,
                "Prophet_Forecast": prophet_forecast_eval[i] if i < len(prophet_forecast_eval) else None,
                "RF_RMSE": rf_rmse,
                "RF_Forecast": rf_forecast_eval[i] if i < len(rf_forecast_eval) else None
            })

        # Append future forecast predictions
        for i, year in enumerate(df_forecast['Year']):
            forecast_summary.append({
                "Country": country,
                "Target": target,
                "Year": year,
                "ARIMA_RMSE": arima_rmse,
                "ARIMA_Forecast": arima_forecast[i] if i < len(arima_forecast) else None,
                "Prophet_RMSE": prophet_rmse,
                "Prophet_Forecast": prophet_forecast[i] if i < len(prophet_forecast) else None,
                "RF_RMSE": rf_rmse,
                "RF_Forecast": rf_forecast[i] if i < len(rf_forecast) else None
            })

df_model_comparison = pd.DataFrame(forecast_summary).sort_values(["Country", "Target", "Year"])

def plot_target_forecast(df_model_all, df_eval_ready, country, target):
    eval_years = [2021, 2022, 2023]
    forecast_years = list(range(2024, 2075))
    full_years = eval_years + forecast_years

    df_actual = df_eval_ready[
        (df_eval_ready['Country'] == country) &
        (df_eval_ready['Year'].isin(eval_years))
    ][['Year', target]].sort_values('Year')

    df_plot = df_model_all[
        (df_model_all['Country'] == country) &
        (df_model_all['Target'] == target) &
        (df_model_all['Year'].isin(full_years))
    ].sort_values('Year')

    years = df_plot['Year'].values
    rf_vals = df_plot['RF_Forecast'].values
    arima_vals = df_plot['ARIMA_Forecast'].values
    prophet_vals = df_plot['Prophet_Forecast'].values

    # Build actual line
    actual_line = []
    for yr in years:
        val = df_actual[df_actual['Year'] == yr][target]
        actual_line.append(val.values[0] if not val.empty else np.nan)

    # Plot
    plt.figure(figsize=(13, 6))
    plt.axvspan(2024, 2074, color='gray', alpha=0.12, label='Forecast Horizon')
    plt.plot(years, actual_line, label="🟧 Actual", color='orange', linewidth=2)
    plt.plot(years, rf_vals, label="🔵 RF Prediction", color='dodgerblue', linewidth=2)
    plt.plot(years, arima_vals, label="🟩 ARIMA Prediction", color='forestgreen', linewidth=2)
    plt.plot(years, prophet_vals, label="🟣 Prophet Prediction", color='darkorchid', linewidth=2)
    plt.title(f"{target} — Actual & Forecast Comparison ({country})", fontsize=16)
    plt.xlabel("Year")
    plt.ylabel("Value")
    plt.grid(True)
    plt.legend()
    plt.tight_layout()
    plt.show()


selected_countries = [
    'United States', 'Germany', 'Japan', 'Brazil', 'India',
    'Indonesia', 'Nigeria', 'Kenya', 'Mexico', 'Bangladesh'
]
selected_targets = ["Life expectancy", "Diabetes", "Cardiovascular diseases"]

for country in selected_countries:
    for target in selected_targets:
        plot_target_forecast(df_model_comparison, df_forecast_ready, country, target)

INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/xcn15p9f.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/fq7s2wc4.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=56323', 'data', 'file=/tmp/tmprjkocm4m/xcn15p9f.json', 'init=/tmp/tmprjkocm4m/fq7s2wc4.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelv0_il2me/prophet_model-20250723150106.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
15:01:06 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
15:01:08 - cmdstanpy - INFO - Chain [1] done processing
INFO:cmdstanpy:Chain [1] done processing
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/3i0cknr4.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/n0ji9b3r.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=90929', 'data', 'file=/tmp/tmprjkocm4m/3i0cknr4.json', 'init=/tmp/tmprjkocm4m/n0ji9b3r.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelg7_3e6i4/prophet_model-20250723150110.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
15:01:10 - cmdstanpy - INFO - Chain [1] start processing
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15:01:10 - cmdstanpy - INFO - Chain [1] done processing
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/94b6i4ty.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/wiku0dsr.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=97008', 'data', 'file=/tmp/tmprjkocm4m/94b6i4ty.json', 'init=/tmp/tmprjkocm4m/wiku0dsr.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelh4lwn385/prophet_model-20250723150111.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
15:01:11 - cmdstanpy - INFO - Chain [1] start processing
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/qg7p85e3.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/4sctqmvy.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=44341', 'data', 'file=/tmp/tmprjkocm4m/qg7p85e3.json', 'init=/tmp/tmprjkocm4m/4sctqmvy.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelw54y1lrb/prophet_model-20250723150112.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
15:01:12 - cmdstanpy - INFO - Chain [1] start processing
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15:01:13 - cmdstanpy - INFO - Chain [1] done processing
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/jhcw5fgj.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/gee4nwbv.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=89937', 'data', 'file=/tmp/tmprjkocm4m/jhcw5fgj.json', 'init=/tmp/tmprjkocm4m/gee4nwbv.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelpy6sfrua/prophet_model-20250723150113.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
15:01:13 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
15:01:13 - cmdstanpy - INFO - Chain [1] done processing
INFO:cmdstanpy:Chain [1] done processing
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/hya61vxx.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/gqd80ecs.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=95356', 'data', 'file=/tmp/tmprjkocm4m/hya61vxx.json', 'init=/tmp/tmprjkocm4m/gqd80ecs.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modeltqfulq0n/prophet_model-20250723150114.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
15:01:14 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
15:01:14 - cmdstanpy - INFO - Chain [1] done processing
INFO:cmdstanpy:Chain [1] done processing
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/ko5xb_ld.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/41l5k7jf.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=6632', 'data', 'file=/tmp/tmprjkocm4m/ko5xb_ld.json', 'init=/tmp/tmprjkocm4m/41l5k7jf.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelwh4bbhju/prophet_model-20250723150115.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
15:01:15 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
15:01:15 - cmdstanpy - INFO - Chain [1] done processing
INFO:cmdstanpy:Chain [1] done processing
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/brryefbv.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/79kzkg23.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=45981', 'data', 'file=/tmp/tmprjkocm4m/brryefbv.json', 'init=/tmp/tmprjkocm4m/79kzkg23.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_model8a83zuam/prophet_model-20250723150115.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
15:01:15 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/1vtpzvl4.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/zo3hc0yn.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=27575', 'data', 'file=/tmp/tmprjkocm4m/1vtpzvl4.json', 'init=/tmp/tmprjkocm4m/zo3hc0yn.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelyvq_dlwd/prophet_model-20250723150116.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
15:01:16 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
15:01:16 - cmdstanpy - INFO - Chain [1] done processing
INFO:cmdstanpy:Chain [1] done processing
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/e7rkf5ty.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/iqbldoj4.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=39930', 'data', 'file=/tmp/tmprjkocm4m/e7rkf5ty.json', 'init=/tmp/tmprjkocm4m/iqbldoj4.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_model2c3lh1fk/prophet_model-20250723150117.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
15:01:17 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
15:01:17 - cmdstanpy - INFO - Chain [1] done processing
INFO:cmdstanpy:Chain [1] done processing
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/yq_o5a0j.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/q586xxt7.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=49176', 'data', 'file=/tmp/tmprjkocm4m/yq_o5a0j.json', 'init=/tmp/tmprjkocm4m/q586xxt7.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_model47zxobpr/prophet_model-20250723150117.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
15:01:17 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
15:01:18 - cmdstanpy - INFO - Chain [1] done processing
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/4a97gqcs.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/b2zn8__y.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=25471', 'data', 'file=/tmp/tmprjkocm4m/4a97gqcs.json', 'init=/tmp/tmprjkocm4m/b2zn8__y.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_model_33hc8lw/prophet_model-20250723150118.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
15:01:18 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
15:01:18 - cmdstanpy - INFO - Chain [1] done processing
INFO:cmdstanpy:Chain [1] done processing
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/buq0wfdv.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/en47nn7i.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=33930', 'data', 'file=/tmp/tmprjkocm4m/buq0wfdv.json', 'init=/tmp/tmprjkocm4m/en47nn7i.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_model05mkl102/prophet_model-20250723150119.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
15:01:19 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
15:01:19 - cmdstanpy - INFO - Chain [1] done processing
INFO:cmdstanpy:Chain [1] done processing
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/6k7ywp4j.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/s5jjzobg.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=4964', 'data', 'file=/tmp/tmprjkocm4m/6k7ywp4j.json', 'init=/tmp/tmprjkocm4m/s5jjzobg.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_model_qx7pexn/prophet_model-20250723150120.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
15:01:20 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
15:01:20 - cmdstanpy - INFO - Chain [1] done processing
INFO:cmdstanpy:Chain [1] done processing
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/4tykcuex.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/4uz180rf.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=19771', 'data', 'file=/tmp/tmprjkocm4m/4tykcuex.json', 'init=/tmp/tmprjkocm4m/4uz180rf.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modeleq3wjnzq/prophet_model-20250723150121.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
15:01:21 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
15:01:21 - cmdstanpy - INFO - Chain [1] done processing
INFO:cmdstanpy:Chain [1] done processing
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/dub9k5jc.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/a_8fsjbb.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=27947', 'data', 'file=/tmp/tmprjkocm4m/dub9k5jc.json', 'init=/tmp/tmprjkocm4m/a_8fsjbb.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_model8cz227c3/prophet_model-20250723150122.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
15:01:22 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
15:01:23 - cmdstanpy - INFO - Chain [1] done processing
INFO:cmdstanpy:Chain [1] done processing
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/e2sg94bz.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/ma6e3h6o.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=59076', 'data', 'file=/tmp/tmprjkocm4m/e2sg94bz.json', 'init=/tmp/tmprjkocm4m/ma6e3h6o.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modellx89hbtk/prophet_model-20250723150123.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
15:01:23 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
15:01:24 - cmdstanpy - INFO - Chain [1] done processing
INFO:cmdstanpy:Chain [1] done processing
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/2foc1vzw.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/3p1hzrs8.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=25301', 'data', 'file=/tmp/tmprjkocm4m/2foc1vzw.json', 'init=/tmp/tmprjkocm4m/3p1hzrs8.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelnfinlbmz/prophet_model-20250723150124.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
15:01:24 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
15:01:24 - cmdstanpy - INFO - Chain [1] done processing
INFO:cmdstanpy:Chain [1] done processing
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/4w6q0v9n.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/ttzzynt3.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=40853', 'data', 'file=/tmp/tmprjkocm4m/4w6q0v9n.json', 'init=/tmp/tmprjkocm4m/ttzzynt3.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_model5g2l9frw/prophet_model-20250723150125.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
15:01:25 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
15:01:25 - cmdstanpy - INFO - Chain [1] done processing
INFO:cmdstanpy:Chain [1] done processing
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/q6sitoku.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/jxnz0p82.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=45329', 'data', 'file=/tmp/tmprjkocm4m/q6sitoku.json', 'init=/tmp/tmprjkocm4m/jxnz0p82.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelbomqvztx/prophet_model-20250723150125.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
15:01:25 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
15:01:26 - cmdstanpy - INFO - Chain [1] done processing
INFO:cmdstanpy:Chain [1] done processing
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/sxj4mx1i.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/j3ff4el7.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=55135', 'data', 'file=/tmp/tmprjkocm4m/sxj4mx1i.json', 'init=/tmp/tmprjkocm4m/j3ff4el7.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_model0a2p2g90/prophet_model-20250723150126.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
15:01:26 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
15:01:26 - cmdstanpy - INFO - Chain [1] done processing
INFO:cmdstanpy:Chain [1] done processing
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/gpjbd4kf.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/4be0jlhe.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=64708', 'data', 'file=/tmp/tmprjkocm4m/gpjbd4kf.json', 'init=/tmp/tmprjkocm4m/4be0jlhe.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelj_13ox_d/prophet_model-20250723150127.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
15:01:27 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
15:01:27 - cmdstanpy - INFO - Chain [1] done processing
INFO:cmdstanpy:Chain [1] done processing
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/tyabzpsb.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/dsimj0a7.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=75251', 'data', 'file=/tmp/tmprjkocm4m/tyabzpsb.json', 'init=/tmp/tmprjkocm4m/dsimj0a7.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_model416aph0l/prophet_model-20250723150128.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
15:01:28 - cmdstanpy - INFO - Chain [1] start processing
INFO:cmdstanpy:Chain [1] start processing
15:01:28 - cmdstanpy - INFO - Chain [1] done processing
INFO:cmdstanpy:Chain [1] done processing
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/8eok89ec.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/ngwetz9c.json
DEBUG:cmdstanpy:idx 0
DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=13514', 'data', 'file=/tmp/tmprjkocm4m/8eok89ec.json', 'init=/tmp/tmprjkocm4m/ngwetz9c.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_model6ra7svlu/prophet_model-20250723150128.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/ctysfzxc.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/py2f0mie.json
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DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=79130', 'data', 'file=/tmp/tmprjkocm4m/ctysfzxc.json', 'init=/tmp/tmprjkocm4m/py2f0mie.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelfzuldb6m/prophet_model-20250723150129.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/sorq_sl7.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/3cpdd1hk.json
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DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=48566', 'data', 'file=/tmp/tmprjkocm4m/sorq_sl7.json', 'init=/tmp/tmprjkocm4m/3cpdd1hk.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_model5qngdowd/prophet_model-20250723150130.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:cmdstanpy:Chain [1] start processing
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/ts10rp61.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/9xnd_9dl.json
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DEBUG:cmdstanpy:running CmdStan, num_threads: None
DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=2376', 'data', 'file=/tmp/tmprjkocm4m/ts10rp61.json', 'init=/tmp/tmprjkocm4m/9xnd_9dl.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_model4cm1tcdf/prophet_model-20250723150131.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/ki_scwy0.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/pvgasxyf.json
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DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=23559', 'data', 'file=/tmp/tmprjkocm4m/ki_scwy0.json', 'init=/tmp/tmprjkocm4m/pvgasxyf.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelzzp4uuj8/prophet_model-20250723150131.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/jg311jj0.json
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DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=9497', 'data', 'file=/tmp/tmprjkocm4m/jg311jj0.json', 'init=/tmp/tmprjkocm4m/2bsjz2e7.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelqgx6y8ti/prophet_model-20250723150132.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/v5h3o2i9.json
DEBUG:cmdstanpy:input tempfile: /tmp/tmprjkocm4m/mpabi3zd.json
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DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.11/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=27575', 'data', 'file=/tmp/tmprjkocm4m/v5h3o2i9.json', 'init=/tmp/tmprjkocm4m/mpabi3zd.json', 'output', 'file=/tmp/tmprjkocm4m/prophet_modelj5nlrp04/prophet_model-20250723150133.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']
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QQ Plot of Residuals, Residuals vs. Fitted Values Plot¶

Histogram and KDE Plot¶

Boxplot¶

Spearman Correlation¶

Feature Selection Comparison (Summary and Charts)¶

Feature selection Comparison with R sq, MAPE, MSE (Summary and Charts)¶

Comparative Summary Table - Feature Selection with metrics (RMSE, MAPE, and R²)¶

Feature Importance Plot¶

ACF and PACF plot¶

Residual diagnostics (heteroscedasticity, autocorrelation)¶

Residual Diagrams¶

Heteroscedasticity and Autocorrelation Consistent (HAC)¶

Forecast Comparison of ARIMA, Prophet, and Random Forest Across Three Targets¶

Time Series Forecasting with Walk-Forward Validation using ARIMA, Prophet, and Random Forests (RMSE Evaluation)¶

Rolling Forecast Validation (Walk-Forward)¶

Result of Rolling Forecast Validation Summary Table:¶

Final Model Training & Forecasting | Evaluation metrics (RMSE, MAPE, R²)¶

Summary of Best Models per Country and Target¶

Evaluation metrics (RMSE, MAPE, R²)¶

	Cardiovascular diseases				Diabetes				Life expectancy				Cardiovascular diseases	Diabetes	Life expectancy
	Forward	RFE	LASSO	Random Forest	Forward	RFE	LASSO	Random Forest	Forward	RFE	LASSO	Random Forest	Combined	Combined	Combined
Child mortality rate	0.0000	0.0000	0.0000	0.0031	0.0000	0.0000	0.0000	0.0083	1.0000	1.0000	18.7165	0.9229	0.0031	0.0083	21.6394
Child mortality rate_lag3	0.0000	0.0000	0.0000	0.0018	0.0000	0.0000	0.1466	0.0060	1.0000	1.0000	7.5788	0.0210	0.0018	0.1526	9.5998
Child mortality rate_lag2	0.0000	0.0000	0.0000	0.0027	0.0000	0.0000	0.0000	0.0054	1.0000	1.0000	0.0000	0.0053	0.0027	0.0054	2.0053
Income_lag3	0.0000	0.0000	0.0000	0.0666	1.0000	0.0000	0.3318	0.0109	1.0000	0.0000	0.3482	0.0020	0.0666	1.3427	1.3502
GDP	0.0000	1.0000	0.0000	0.0878	1.0000	1.0000	0.3975	0.0254	1.0000	0.0000	0.3190	0.0021	1.0878	2.4229	1.3211
BMI_avg	1.0000	0.0000	0.0000	0.0062	0.0000	0.0000	0.0000	0.0106	1.0000	0.0000	0.1729	0.0026	1.0062	0.0106	1.1755
Sex ratio	0.0000	0.0000	0.0000	0.0076	1.0000	0.0000	0.0923	0.0054	1.0000	0.0000	0.1535	0.0024	0.0076	1.0977	1.1559
Median age_lag3	0.0000	0.0000	0.0000	0.0060	1.0000	0.0000	0.3257	0.0281	1.0000	0.0000	0.1433	0.0073	0.0060	1.3538	1.1506
Sex ratio_lag3	0.0000	0.0000	0.0000	0.0262	1.0000	0.0000	0.1618	0.0071	1.0000	0.0000	0.0936	0.0023	0.0262	1.1689	1.0959
Sex ratio_lag2	0.0000	0.0000	0.0000	0.0108	1.0000	0.0000	0.0150	0.0048	1.0000	0.0000	0.0793	0.0019	0.0108	1.0198	1.0812
Sex ratio_lag1	0.0000	0.0000	0.0000	0.0095	1.0000	0.0000	0.0165	0.0045	1.0000	0.0000	0.0246	0.0016	0.0095	1.0210	1.0262
BMI_avg_lag3	1.0000	1.0000	0.0000	0.0558	1.0000	1.0000	3.3170	0.5842	1.0000	0.0000	0.0000	0.0018	2.0558	5.9012	1.0018
GDP_lag3	0.0000	0.0000	0.0000	0.1160	1.0000	0.0000	0.2146	0.0263	1.0000	0.0000	0.0000	0.0016	0.1160	1.2409	1.0016
GDP_lag1	0.0000	0.0000	0.0000	0.0272	1.0000	0.0000	0.0000	0.0150	1.0000	0.0000	0.0000	0.0013	0.0272	1.0150	1.0013
BMI_avg_lag1	1.0000	0.0000	0.0000	0.0098	0.0000	0.0000	0.0000	0.0083	1.0000	0.0000	0.0000	0.0013	1.0098	0.0083	1.0013
GDP_lag2	0.0000	0.0000	0.0000	0.0206	1.0000	0.0000	0.0000	0.0172	1.0000	0.0000	0.0000	0.0012	0.0206	1.0172	1.0012
BMI_avg_lag2	0.0000	0.0000	0.0000	0.0165	0.0000	0.0000	0.0000	0.0103	1.0000	0.0000	0.0000	0.0011	0.0165	0.0103	1.0011
Income	0.0000	1.0000	0.0000	0.0522	1.0000	1.0000	0.5190	0.0574	0.0000	0.0000	0.3121	0.0025	1.0522	2.5764	0.3146
Unemployment Rate	0.0000	0.0000	0.0000	0.0105	0.0000	0.0000	0.0432	0.0156	0.0000	0.0000	0.0668	0.0025	0.0105	0.0588	0.0693
Cost of a healthy diet	0.0000	0.0000	0.0000	0.0170	1.0000	0.0000	0.3548	0.0223	0.0000	0.0000	0.0505	0.0013	0.0170	1.3771	0.0518
Incomplete tertiary education	0.0000	0.0000	0.0000	0.2616	0.0000	0.0000	0.3349	0.0377	0.0000	0.0000	0.0203	0.0043	0.2616	0.3726	0.0246
Unemployment Rate_lag3	0.0000	0.0000	0.0000	0.0230	0.0000	0.0000	0.0348	0.0190	0.0000	0.0000	0.0000	0.0014	0.0230	0.0538	0.0014
Unemployment Rate_lag1	0.0000	0.0000	0.0000	0.0099	0.0000	0.0000	0.0002	0.0112	0.0000	0.0000	0.0000	0.0013	0.0099	0.0114	0.0013
Cost of a healthy diet_lag3	0.0000	0.0000	0.0000	0.0134	0.0000	0.0000	0.0383	0.0104	0.0000	0.0000	0.0000	0.0013	0.0134	0.0487	0.0013
Income_lag2	0.0000	0.0000	0.0000	0.0620	1.0000	0.0000	0.0522	0.0116	0.0000	0.0000	0.0000	0.0012	0.0620	1.0638	0.0012
Cost of a healthy diet_lag2	0.0000	0.0000	0.0000	0.0174	1.0000	0.0000	0.0367	0.0054	0.0000	0.0000	0.0000	0.0011	0.0174	1.0421	0.0011
Income_lag1	0.0000	0.0000	0.0000	0.0371	1.0000	0.0000	0.0774	0.0147	0.0000	0.0000	0.0000	0.0011	0.0371	1.0921	0.0011
Unemployment Rate_lag2	0.0000	0.0000	0.0000	0.0124	0.0000	0.0000	0.0525	0.0104	0.0000	0.0000	0.0000	0.0011	0.0124	0.0629	0.0011
Cost of a healthy diet_lag1	0.0000	0.0000	0.0000	0.0094	1.0000	0.0000	0.0000	0.0066	0.0000	0.0000	0.0000	0.0011	0.0094	1.0066	0.0011